From 26bba92e34dc2c1dda2828ce598a234099dd0685 Mon Sep 17 00:00:00 2001 From: Okba BEKHELIFI Date: Wed, 8 Jan 2025 16:02:18 +0100 Subject: [PATCH 1/5] - Added Timeseries example: - SSVEPFormer for classification of EEG data for Brain-Computer Interface. On branch master Your branch is up to date with 'origin/master'. Changes to be committed: new file: examples/timeseries/eeg_bci_ssvepformer.py --- examples/timeseries/eeg_bci_ssvepformer.py | 656 +++++++++++++++++++++ 1 file changed, 656 insertions(+) create mode 100644 examples/timeseries/eeg_bci_ssvepformer.py diff --git a/examples/timeseries/eeg_bci_ssvepformer.py b/examples/timeseries/eeg_bci_ssvepformer.py new file mode 100644 index 0000000000..40d1a41f92 --- /dev/null +++ b/examples/timeseries/eeg_bci_ssvepformer.py @@ -0,0 +1,656 @@ +""" +Title: Electroencephalogram Signal Classification for Brain-Computer Interface +Author: [Okba Bekhelifi](https://github.com/okbalefthanded) +Date created: 2025/01/08 +Last modified: 2025/01/08 +Description: A Transformer based classification for EEG signal for BCI. +Accelerator: GPU +""" + +""" +# Introduction + +This tutorial will explain how to build a Transformer based Neural Network to classify +Brain-Computer Interface (BCI) Electroencephalograpy (EEG) data recorded in a +Steady-State Visual Evoked Potentials (SSVEPs) experiment for the application of a +brain-controlled speller. + +The tutorial reproduces an experiment from the SSVEPFormer study [1] +( [arXiv preprint](https://arxiv.org/abs/2210.04172) / +[Peer-Reviewed paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023002319) ). +This model was the first Transformer based model to be introduced for SSVEP data classification, +we will test it on the Nakanishi et al. [2] public dataset as dataset 1 from the paper. + +The process follows an inter-subject classification experiment. Given N subject data in +the dataset, the training data partition contains data from N-1 subject and the remaining +single subject data is used for testing. the training set does not contain any sample from +the testing subject. This way we construct a true subject-independent model. We keep the +same parameters and settings as the original paper in all processing operations from +preprocessing to training. + + +The tutorial begins with a quick BCI and dataset descprition then, we go through the +technicalities following these sections: +- Setup, and imports. +- Dataset download and extraction. +- Data preprocessing: EEG data filtering, segmentation and visualization of raw and +filtered data, and frequency response for a well performing participant. +- Layers and model creation. +- Evaluation: a single participant data classification as an example then the total +participants data classification. +- Visulization: we show the results of training and inference times comparison among +the Keras 3 available backends (JAX, Tensorflow, and PyTorch) on three different GPUs. +- Conclusion: final discussion and remarks. + +""" + +""" +# Dataset description + +## BCI and SSVEP: +A BCI offers the ability to communicate using only brain activity, this can be achieved +through exogenous stimuli that generate specific responses indicating the intent of the +subject. the responses are elicited when the user focuses their attention on the target +stimulus. We can use visual stimuli by presenting the subject with a set of options +typically on a monitor as a grid to select one command at a time. Each stimulus will +flicker following a fixed frequency and phase, the resulting EEG recorded at occipital +and occipito-parietal areas of the cortex (visual cortex) will have higher power in the +associated frequency with the stimulus where the subject was looking at. This type of +BCI paradigm is called the Steady-State Visual Evoked Potentials (SSVEPs) and became +widely used for multiple application due to its reliability and high perfromance in +classification and rapidity as a 1-second of EEG is sufficient making a command. Other +types of brain responses exists and do not require external stimulations, however they +are less reliable. +[Demo video](https://www.youtube.com/watch?v=VtA6jsEMIug) + +This tutorials uses the 12 commands (class) public SSVEP dataset [2] with the following +interface emulating a phone dialing numbers. +![Imgur](https://imgur.com/qQu5bdQ.jpg) + +The dataset was recorded with 10 participants, each faced the above 12 SSVEP stimuli (A). +The stimulation frequencies ranged from 9.25Hz to 14.75 Hz with 0.5Hz step, and phases +ranged from 0 to 1.5 π with 0.5 π step for each row.(B). The EEG signal was acquired +with 8 electrodes (channels) (PO7, PO3, POz, +PO4, PO8, O1, Oz, O2) sampling frequency was 2048 Hz then the stored data were +downsampled to 256 Hz. The subjects completed 15 blocks of recordings, each consisted +of 12 random ordered stimulations (1 for each class) of 4 seconds each. In total, +each subject conducted 180 trials. + + +""" + +""" +# Setup +""" + +""" +## Select JAX backend + +""" + +import os + +os.environ["KERAS_BACKEND"] = "jax" + +""" +## Install dependencies + +""" + +"""shell +pip install -q numpy +pip install -q scipy +pip install -q matplotlib +""" + +""" +# Imports + + +""" + +# deep learning libraries +from keras import backend as K +from keras import layers +import keras + +# visualization and signal processing imports +import matplotlib.pyplot as plt +import tensorflow as tf +import numpy as np +from scipy.signal import butter, filtfilt +from scipy.io import loadmat + +# setting the backend, seed and Keras channel format +K.set_image_data_format("channels_first") +keras.utils.set_random_seed(42) + +""" +# Download and extract dataset + + +""" + +""" +## Nakanishi et. al 2015 [DataSet Repo](https://github.com/mnakanishi/12JFPM_SSVEP) +""" + +"""shell +curl -O https://sccn.ucsd.edu/download/cca_ssvep.zip +unzip cca_ssvep.zip +""" + +""" +# Pre-Processing + +The preprocessing steps followed are first to read the EEG data for each subject, then +to filter the raw data in a frequency interval where most useful information lies, +then we select a fixed duration of signal starting from the onset of the stimulation +(due to latency delay caused by the visual system we start we add 135 milliseconds to +the stimulation onset). Lastly, all subjects data are concatenated in a single Tensor +of the shape: [subjects x samples x channels x trials]. The data labels are also +concatenated following the order of the trials in the experiments and will be a +matrix of the shape [subjects x trials] +(here by channels we mean electrodes, we use this notation throughout the tutorial). +""" + + +def raw_signal(folder, fs=256, duration=1.0, onset=0.135): + """selecting a 1-second segment of the raw EEG signal for + subject 1. + """ + onset = 38 + int(onset * fs) + end = int(duration * fs) + data = loadmat(f"{folder}/s1.mat") + # samples, channels, trials, targets + eeg = data["eeg"].transpose((2, 1, 3, 0)) + # segment data + eeg = eeg[onset : onset + end, :, :, :] + return eeg + + +def segment_eeg( + folder, elecs=None, fs=256, duration=1.0, band=[5.0, 45.0], order=4, onset=0.135 +): + """Filtering and segmenting EEG signals for all subjects.""" + n_subejects = 10 + onset = 38 + int(onset * fs) + end = int(duration * fs) + X, Y = [], [] # empty data and labels + + for subj in range(1, n_subejects + 1): + data = loadmat(f"{data_folder}/s{subj}.mat") + # samples, channels, trials, targets + eeg = data["eeg"].transpose((2, 1, 3, 0)) + # filter data + eeg = filter_eeg(eeg, fs=fs, band=band, order=order) + # segment data + eeg = eeg[onset : onset + end, :, :, :] + # reshape labels + samples, channels, blocks, targets = eeg.shape + y = np.tile(np.arange(1, targets + 1), (blocks, 1)) + y = y.reshape((1, blocks * targets), order="F") + + X.append(eeg.reshape((samples, channels, blocks * targets), order="F")) + Y.append(y) + + X = np.array(X, dtype=np.float32, order="F") + Y = np.array(Y, dtype=np.float32).squeeze() + + return X, Y + + +def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4): + """Filter EEG signal using a zero-phase IIR filter""" + B, A = butter(order, np.array(band) / (fs / 2), btype="bandpass") + return filtfilt(B, A, data, axis=0) + + +""" +## Segment data into epochs +""" + +data_folder = os.path.abspath("./cca_ssvep") +band = [8, 64] # low-frequency / high-frequency cutoffS +order = 4 # filter order +fs = 256 # sampling frequency +duration = 1.0 # 1 second + +# raw signal +X_raw = raw_signal(data_folder, fs=fs, duration=duration) +print( + f"A single subject raw EEG (X_raw) shape: {X_raw.shape} [Samples x Channels x Blocks x Targets]" +) + +# segmented signal +X, Y = segment_eeg(data_folder, band=band, order=order, fs=fs, duration=duration) +print( + f"Full training data (X) shape: {X.shape} [Subject x Samples x Channels x Trials]" +) +print(f"data labels (Y) shape: {Y.shape} [Subject x Trials]") + +samples = X.shape[1] +time = np.linspace(0.0, samples / fs, samples) * 1000 + +""" +## Visualize EEG signal +""" + +""" +## EEG in time + +Raw EEG vs Filtered EEG +the same 1-second recording for subject s1 at Oz (central electrode in the visual cortex, +back of the head) is illustrated. left is the raw EEG as recorded and in the right is +the filtered EEG on the [8, 64] Hz frequency band. we see less noise and +normalized amplitude values in a natural EEG range. +""" + + +elec = 6 # Oz channel + +x_label = "Time (ms)" +y_label = "Voltage (uV)" +# Create subplots +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) + +# Plot data on the first subplot +ax1.plot(time, X_raw[:, elec, 0, 0], "r-") +ax1.set_xlabel(x_label) +ax1.set_ylabel(y_label) +ax1.set_title("Raw EEG : 1 second at Oz ") +# ax1.legend() + +# Plot data on the second subplot +ax2.plot(time, X[0, :, elec, 0], "b-") +ax2.set_xlabel(x_label) +ax2.set_ylabel(y_label) +ax2.set_title("Filtered EEG between 8-64 Hz: 1 second at Oz") +# ax2.legend() + +# Adjust spacing between subplots +plt.tight_layout() + +# Show the plot +plt.show() + +""" +## EEG frequency representation + +Using the welch method, we visualize the frequency power for a well performing subject +for the entire 4 seconds EEG recording at Oz electrode for each stimuli. the red peaks +indicate the stimuli fundamental frequency and the 2nd harmonics (double the fundamental +frequency). we see clear peaks showing the high responses from that subject which means +that this subject is a good candidate for SSVEP BCI control. In many cases the peaks +are weak or absent, meaning that subject do not achieve the task correctly. + +![eeg_frequency](https://i.imgur.com/IlGEwfn.png) +""" + + +""" +# Create Layers and model + +Create Layers in a cross-framework custom component fashion. +In the SSVEPFormer, the data is first transformed to the frequency domain through +Fast-Fourier transform (FFT), to construct a complex spectrum presentation consisting of +the concatenation of frequency and phase information in a fixed frequency band. To keep +the model in an end-to-end format, we implement the complex spectrum transformation as +non-trainable layer. + +![model](https://imgur.com/r7ZUpQJ.jpg) +The SSVEPFormer unlike the Transformer architecture does not contain positional encoding/embedding +layers which replaced a channel combination block that has a layer of Conv1D layer of 1 +kernel size with double input channels (double the count of electrodes) number of filters, +and LayerNorm, Gelu activation and dropout. +Another difference with Transformers is the absence of multi-head attention layers with +attention mechanism. +The model encoder contains two identical and successive blocks. Each block has two +sub-blocks of CNN module and MLP module. the CNN module consists of a LayerNorm, Conv1D +with the same number of filters as channel combination, LayerNorm, Gelu, Dropout and an +residual connection. The MLP module consists of a LayerNorm, Dense layer, Gelu, droput +and residual connection. the Dense layer is applied on each channel separately. +The last block of the model is MLP head with Flatten layer, Dropout, Dense, LayerNorm, +Gelu, Dropout and Dense layer with softmax acitvation. +All trainable weights are initialized by a normal distribution with 0 mean and 0.01 +standard deviation as state in the original paper. +""" + + +class ComplexSpectrum(keras.layers.Layer): + def __init__(self, nfft=512, fft_start=8, fft_end=64): + super().__init__() + self.nfft = nfft + self.fft_start = fft_start + self.fft_end = fft_end + + def call(self, x): + samples = x.shape[-1] + x = keras.ops.rfft(x, fft_length=self.nfft) + real = x[0] / samples + imag = x[1] / samples + real = real[:, :, self.fft_start : self.fft_end] + imag = imag[:, :, self.fft_start : self.fft_end] + x = keras.ops.concatenate((real, imag), axis=-1) + return x + + +class ChannelComb(keras.layers.Layer): + def __init__(self, n_channels, drop_rate=0.5): + super().__init__() + self.conv = layers.Conv1D( + 2 * n_channels, + 1, + padding="same", + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.normalization = layers.LayerNormalization() + self.activation = layers.Activation(activation="gelu") + self.drop = layers.Dropout(drop_rate) + + def call(self, x): + x = self.conv(x) + x = self.normalization(x) + x = self.activation(x) + x = self.drop(x) + return x + + +class ConvAttention(keras.layers.Layer): + def __init__(self, n_channels, drop_rate=0.5): + super().__init__() + self.norm = layers.LayerNormalization() + self.conv = layers.Conv1D( + 2 * n_channels, + 31, + padding="same", + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.activation = layers.Activation(activation="gelu") + self.drop = layers.Dropout(drop_rate) + + def call(self, x): + input = x + x = self.norm(x) + x = self.conv(x) + x = self.activation(x) + x = self.drop(x) + x = x + input + return x + + +class ChannelMLP(keras.layers.Layer): + def __init__(self, n_features, drop_rate=0.5): + super().__init__() + self.norm = layers.LayerNormalization() + self.mlp = layers.Dense( + 2 * n_features, + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.activation = layers.Activation(activation="gelu") + self.drop = layers.Dropout(drop_rate) + self.cat = layers.Concatenate(axis=1) + + def call(self, x): + input = x + channels = x.shape[1] # x shape : NCF + x = self.norm(x) + output_channels = [] + for i in range(channels): + c = self.mlp(x[:, :, i]) + c = layers.Reshape([1, -1])(c) + output_channels.append(c) + x = self.cat(output_channels) + x = self.activation(x) + x = self.drop(x) + x = x + input + return x + + +class Encoder(keras.layers.Layer): + def __init__(self, n_channels, n_features, drop_rate=0.5): + super().__init__() + self.attention1 = ConvAttention(n_channels, drop_rate=drop_rate) + self.mlp1 = ChannelMLP(n_features, drop_rate=drop_rate) + self.attention2 = ConvAttention(n_channels, drop_rate=drop_rate) + self.mlp2 = ChannelMLP(n_features, drop_rate=drop_rate) + + def call(self, x): + x = self.attention1(x) + x = self.mlp1(x) + x = self.attention2(x) + x = self.mlp2(x) + return x + + +class MlpHead(keras.layers.Layer): + def __init__(self, n_classes, drop_rate=0.5): + super().__init__() + self.flatten = layers.Flatten() + self.drop = layers.Dropout(drop_rate) + self.linear1 = layers.Dense( + 6 * n_classes, + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.norm = layers.LayerNormalization() + self.activation = layers.Activation(activation="gelu") + self.drop2 = layers.Dropout(drop_rate) + self.linear2 = layers.Dense( + n_classes, + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + + def call(self, x): + x = self.flatten(x) + x = self.drop(x) + x = self.linear1(x) + x = self.norm(x) + x = self.activation(x) + x = self.drop2(x) + x = self.linear2(x) + return x + + +""" +### Create a sequential model with the layers above +""" + + +def create_ssvepformer( + input_shape, fs, resolution, fq_band, n_channels, n_classes, drop_rate +): + nfft = round(fs / resolution) + fft_start = int(fq_band[0] / resolution) + fft_end = int(fq_band[1] / resolution) + 1 + n_features = fft_end - fft_start + + model = keras.Sequential( + [ + keras.Input(shape=input_shape), + ComplexSpectrum(nfft, fft_start, fft_end), + ChannelComb(n_channels=n_channels, drop_rate=drop_rate), + Encoder(n_channels=n_channels, n_features=n_features, drop_rate=drop_rate), + Encoder(n_channels=n_channels, n_features=n_features, drop_rate=drop_rate), + MlpHead(n_classes=n_classes, drop_rate=drop_rate), + layers.Activation(activation="softmax"), + ] + ) + + return model + + +""" +# Evaluation +""" + +# Training settings same as the original paper +BATCH_SIZE = 128 +EPOCHS = 100 +LR = 0.001 # learning rate +WD = 0.001 # weight decay +MOMENTUM = 0.9 +DROP_RATE = 0.5 + +resolution = 0.25 + +""" +From the entire dataset we select folds for each subject evaluation. +construct a tf dataset object for train and testing data and create the model and launch +the training using SGD optimizer. +""" + + +def concatenate_subjects(x, y, fold): + X = np.concatenate([x[idx] for idx in fold], axis=-1) + Y = np.concatenate([y[idx] for idx in fold], axis=-1) + X = X.transpose((2, 1, 0)) # trials x channels x samples + return X, Y - 1 # transform labels to values from 0...11 + + +def evaluate_subject( + x_train, + y_train, + x_val, + y_val, + fs=256, + resolution=0.25, + band=[8, 64], + channels=8, + n_classes=12, + drop_rate=DROP_RATE, +): + + train_dataset = ( + tf.data.Dataset.from_tensor_slices((x_train, y_train)) + .batch(BATCH_SIZE) + .prefetch(tf.data.AUTOTUNE) + ) + + test_dataset = ( + tf.data.Dataset.from_tensor_slices((x_val, y_val)) + .batch(BATCH_SIZE) + .prefetch(tf.data.AUTOTUNE) + ) + + model = create_ssvepformer( + input_shape, fs, resolution, band, channels, n_classes, drop_rate + ) + sgd = keras.optimizers.SGD(learning_rate=LR, momentum=MOMENTUM, weight_decay=WD) + + model.compile( + loss="sparse_categorical_crossentropy", + optimizer=sgd, + metrics=["accuracy"], + jit_compile=True, + ) + + history = model.fit( + train_dataset, + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=test_dataset, + verbose=0, + ) + loss, acc = model.evaluate(test_dataset) + return acc * 100 + + +""" +## Run evaluation +""" + +channels = X.shape[2] +samples = X.shape[1] +input_shape = (channels, samples) +n_classes = 12 + +model = create_ssvepformer( + input_shape, fs, resolution, band, channels, n_classes, DROP_RATE +) +model.summary() + +""" +## Evaluation on all subjects following a leave-one-subject out data repartition scheme +""" + +accs = np.zeros(10) + +for subject in range(10): + print(f"Testing subject: {subject+ 1}") + + # create train / test folds + folds = np.delete(np.arange(10), subject) + train_index = folds + test_index = [subject] + + # create data split for each subject + x_train, y_train = concatenate_subjects(X, Y, train_index) + x_val, y_val = concatenate_subjects(X, Y, test_index) + + # train and evaluate a fold and compute the time it takes + acc = evaluate_subject(x_train, y_train, x_val, y_val) + + accs[subject] = acc + +print(f"\nAccuracy Across Subjects: {accs.mean()} % std: {np.std(accs)}") + +""" +and that's it! we see how some subjects with no data on the training set still can achieve +almost a 100% correct commands and others show poor performance around 50%. In the original +paper using PyTorch the average accuracy was 84.04% with 17.37 std. we reached the same +values knowing the stochastic nature of deep learning. +""" + +""" +# Visualizations + +Training and inference times comparison between the different backends (Jax, Tensorflow +and PyTorch) on the three GPUs available with Colab Free/Pro/Pro+: T4, L4, A100. + + +""" + +""" +## Training Time + +![training_time](https://imgur.com/YJlmuoc.png) +""" + +""" +# Inference Time + +![inference_time](https://imgur.com/CRuLku5.png) +""" + +""" +the Jax backend was the best on training and inference in all the GPUs, the PyTorch was +exremely slow due to the jit compilation option being disable because of the complex +data type calculated by FFT which is not supported by the PyTorch jit compiler. +""" + +""" +# Acknowledgment + +I thank Chris Perry [X](https://x.com/thechrisperry) @GoogleColab for supporting this +work with GPU compute. +""" + +""" +# References +[1] Chen, J. et al. (2023) ‘A transformer-based deep neural network model for SSVEP +classification’, Neural Networks, 164, pp. 521–534. Available at: https://doi.org/10.1016/j.neunet.2023.04.045. + +[2] Nakanishi, M. et al. (2015) ‘A Comparison Study of Canonical Correlation Analysis +Based Methods for Detecting Steady-State Visual Evoked Potentials’, Plos One, 10(10), p. +e0140703. Available at: https://doi.org/10.1371/journal.pone.0140703 +""" From 481eea0881c9005dbacbdc4cf6c46a1845307f0d Mon Sep 17 00:00:00 2001 From: Okba BEKHELIFI Date: Sat, 25 Jan 2025 12:03:54 +0100 Subject: [PATCH 2/5] - Updated the BCI SSVEPFormer example after pull request review On branch master Your branch is up to date with 'origin/master'. Changes to be committed: modified: eeg_bci_ssvepformer.py --- examples/timeseries/eeg_bci_ssvepformer.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/examples/timeseries/eeg_bci_ssvepformer.py b/examples/timeseries/eeg_bci_ssvepformer.py index 40d1a41f92..8d02003645 100644 --- a/examples/timeseries/eeg_bci_ssvepformer.py +++ b/examples/timeseries/eeg_bci_ssvepformer.py @@ -29,7 +29,7 @@ preprocessing to training. -The tutorial begins with a quick BCI and dataset descprition then, we go through the +The tutorial begins with a quick BCI and dataset description then, we go through the technicalities following these sections: - Setup, and imports. - Dataset download and extraction. @@ -65,7 +65,7 @@ This tutorials uses the 12 commands (class) public SSVEP dataset [2] with the following interface emulating a phone dialing numbers. -![Imgur](https://imgur.com/qQu5bdQ.jpg) +![dataset](/img/eeg_bci_ssvepformer/eeg_ssvepformer_dataset1_interface.jpg) The dataset was recorded with 10 participants, each faced the above 12 SSVEP stimuli (A). The stimulation frequencies ranged from 9.25Hz to 14.75 Hz with 0.5Hz step, and phases @@ -240,7 +240,7 @@ def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4): ## EEG in time Raw EEG vs Filtered EEG -the same 1-second recording for subject s1 at Oz (central electrode in the visual cortex, +The same 1-second recording for subject s1 at Oz (central electrode in the visual cortex, back of the head) is illustrated. left is the raw EEG as recorded and in the right is the filtered EEG on the [8, 64] Hz frequency band. we see less noise and normalized amplitude values in a natural EEG range. @@ -259,14 +259,12 @@ def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4): ax1.set_xlabel(x_label) ax1.set_ylabel(y_label) ax1.set_title("Raw EEG : 1 second at Oz ") -# ax1.legend() # Plot data on the second subplot ax2.plot(time, X[0, :, elec, 0], "b-") ax2.set_xlabel(x_label) ax2.set_ylabel(y_label) ax2.set_title("Filtered EEG between 8-64 Hz: 1 second at Oz") -# ax2.legend() # Adjust spacing between subplots plt.tight_layout() @@ -284,7 +282,7 @@ def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4): that this subject is a good candidate for SSVEP BCI control. In many cases the peaks are weak or absent, meaning that subject do not achieve the task correctly. -![eeg_frequency](https://i.imgur.com/IlGEwfn.png) +![eeg_frequency](/img/eeg_bci_ssvepformer/eeg_ssvepformer_frequencypowers.png) """ @@ -298,7 +296,7 @@ def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4): the model in an end-to-end format, we implement the complex spectrum transformation as non-trainable layer. -![model](https://imgur.com/r7ZUpQJ.jpg) +![model](/img/eeg_bci_ssvepformer/eeg_ssvepformer_model.jpg) The SSVEPFormer unlike the Transformer architecture does not contain positional encoding/embedding layers which replaced a channel combination block that has a layer of Conv1D layer of 1 kernel size with double input channels (double the count of electrodes) number of filters, @@ -522,6 +520,7 @@ def evaluate_subject( y_train, x_val, y_val, + input_shape, fs=256, resolution=0.25, band=[8, 64], @@ -598,7 +597,7 @@ def evaluate_subject( x_val, y_val = concatenate_subjects(X, Y, test_index) # train and evaluate a fold and compute the time it takes - acc = evaluate_subject(x_train, y_train, x_val, y_val) + acc = evaluate_subject(x_train, y_train, x_val, y_val, input_shape) accs[subject] = acc @@ -623,13 +622,13 @@ def evaluate_subject( """ ## Training Time -![training_time](https://imgur.com/YJlmuoc.png) +![training_time](/img/eeg_bci_ssvepformer/eeg_ssvepformer_keras_training_time.png) """ """ # Inference Time -![inference_time](https://imgur.com/CRuLku5.png) +![inference_time](/img/eeg_bci_ssvepformer/eeg_ssvepformer_keras_inference_time.png) """ """ @@ -653,4 +652,4 @@ def evaluate_subject( [2] Nakanishi, M. et al. (2015) ‘A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials’, Plos One, 10(10), p. e0140703. Available at: https://doi.org/10.1371/journal.pone.0140703 -""" +""" \ No newline at end of file From 98be6b8f37dde5f3ff241a8fd280cbe99f9492d3 Mon Sep 17 00:00:00 2001 From: Okba BEKHELIFI Date: Tue, 28 Jan 2025 10:20:04 +0100 Subject: [PATCH 3/5] - Added files generated by the add_example command after PR review On branch master Your branch is up to date with 'origin/master'. 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+ + 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 + 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 + + + 0 145M 0 49152 0 0 40897 0 1:02:11 0:00:01 1:02:10 40891 + + + 1 145M 1 2480k 0 0 1140k 0 0:02:10 0:00:02 0:02:08 1140k + + + 9 145M 9 13.4M 0 0 4371k 0 0:00:34 0:00:03 0:00:31 4371k + + + 17 145M 17 25.9M 0 0 6201k 0 0:00:24 0:00:04 0:00:20 6200k + + + 25 145M 25 37.2M 0 0 7398k 0 0:00:20 0:00:05 0:00:15 7632k + + + 33 145M 33 48.9M 0 0 8052k 0 0:00:18 0:00:06 0:00:12 9972k + + + 41 145M 41 60.3M 0 0 8586k 0 0:00:17 0:00:07 0:00:10 11.5M + + + 49 145M 49 71.9M 0 0 9014k 0 0:00:16 0:00:08 0:00:08 11.6M + + + 57 145M 57 84.2M 0 0 9423k 0 0:00:15 0:00:09 0:00:06 11.9M + + + 66 145M 66 96.5M 0 0 9709k 0 0:00:15 0:00:10 0:00:05 11.8M + + + 74 145M 74 108M 0 0 9938k 0 0:00:14 0:00:11 0:00:03 12.0M + + + 82 145M 82 119M 0 0 9.8M 0 0:00:14 0:00:12 0:00:02 11.8M + + + 90 145M 90 131M 0 0 9.9M 0 0:00:14 0:00:13 0:00:01 11.8M + + + 98 145M 98 142M 0 0 10.0M 0 0:00:14 0:00:14 --:--:-- 11.7M +100 145M 100 145M 0 0 10.1M 0 0:00:14 0:00:14 --:--:-- 11.7M + + +
+``` +Archive: cca_ssvep.zip + creating: cca_ssvep/ + inflating: cca_ssvep/s4.mat + inflating: cca_ssvep/s5.mat + +``` +
+ +
+``` + inflating: cca_ssvep/s3.mat + inflating: cca_ssvep/s7.mat + +``` +
+ +
+``` + inflating: cca_ssvep/chan_locs.pdf + inflating: cca_ssvep/readme.txt + inflating: cca_ssvep/s2.mat + inflating: cca_ssvep/s8.mat + +``` +
+ +
+``` + inflating: cca_ssvep/s10.mat + +``` +
+ +
+``` + inflating: cca_ssvep/s9.mat + +``` +
+ +
+``` + inflating: cca_ssvep/s6.mat + inflating: cca_ssvep/s1.mat + +``` +
+ + + +# Pre-Processing + +The preprocessing steps followed are first to read the EEG data for each subject, then +to filter the raw data in a frequency interval where most useful information lies, +then we select a fixed duration of signal starting from the onset of the stimulation +(due to latency delay caused by the visual system we start we add 135 milliseconds to +the stimulation onset). Lastly, all subjects data are concatenated in a single Tensor +of the shape: [subjects x samples x channels x trials]. The data labels are also +concatenated following the order of the trials in the experiments and will be a +matrix of the shape [subjects x trials] +(here by channels we mean electrodes, we use this notation throughout the tutorial). + + +```python + +def raw_signal(folder, fs=256, duration=1.0, onset=0.135): + """selecting a 1-second segment of the raw EEG signal for + subject 1. + """ + onset = 38 + int(onset * fs) + end = int(duration * fs) + data = loadmat(f"{folder}/s1.mat") + # samples, channels, trials, targets + eeg = data["eeg"].transpose((2, 1, 3, 0)) + # segment data + eeg = eeg[onset : onset + end, :, :, :] + return eeg + + +def segment_eeg( + folder, elecs=None, fs=256, duration=1.0, band=[5.0, 45.0], order=4, onset=0.135 +): + """Filtering and segmenting EEG signals for all subjects.""" + n_subejects = 10 + onset = 38 + int(onset * fs) + end = int(duration * fs) + X, Y = [], [] # empty data and labels + + for subj in range(1, n_subejects + 1): + data = loadmat(f"{data_folder}/s{subj}.mat") + # samples, channels, trials, targets + eeg = data["eeg"].transpose((2, 1, 3, 0)) + # filter data + eeg = filter_eeg(eeg, fs=fs, band=band, order=order) + # segment data + eeg = eeg[onset : onset + end, :, :, :] + # reshape labels + samples, channels, blocks, targets = eeg.shape + y = np.tile(np.arange(1, targets + 1), (blocks, 1)) + y = y.reshape((1, blocks * targets), order="F") + + X.append(eeg.reshape((samples, channels, blocks * targets), order="F")) + Y.append(y) + + X = np.array(X, dtype=np.float32, order="F") + Y = np.array(Y, dtype=np.float32).squeeze() + + return X, Y + + +def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4): + """Filter EEG signal using a zero-phase IIR filter""" + B, A = butter(order, np.array(band) / (fs / 2), btype="bandpass") + return filtfilt(B, A, data, axis=0) + +``` + +--- +## Segment data into epochs + + +```python +data_folder = os.path.abspath("./cca_ssvep") +band = [8, 64] # low-frequency / high-frequency cutoffS +order = 4 # filter order +fs = 256 # sampling frequency +duration = 1.0 # 1 second + +# raw signal +X_raw = raw_signal(data_folder, fs=fs, duration=duration) +print( + f"A single subject raw EEG (X_raw) shape: {X_raw.shape} [Samples x Channels x Blocks x Targets]" +) + +# segmented signal +X, Y = segment_eeg(data_folder, band=band, order=order, fs=fs, duration=duration) +print( + f"Full training data (X) shape: {X.shape} [Subject x Samples x Channels x Trials]" +) +print(f"data labels (Y) shape: {Y.shape} [Subject x Trials]") + +samples = X.shape[1] +time = np.linspace(0.0, samples / fs, samples) * 1000 +``` + +
+``` +A single subject raw EEG (X_raw) shape: (256, 8, 15, 12) [Samples x Channels x Blocks x Targets] + +Full training data (X) shape: (10, 256, 8, 180) [Subject x Samples x Channels x Trials] +data labels (Y) shape: (10, 180) [Subject x Trials] + +``` +
+--- +## Visualize EEG signal + +--- +## EEG in time + +Raw EEG vs Filtered EEG +The same 1-second recording for subject s1 at Oz (central electrode in the visual cortex, +back of the head) is illustrated. left is the raw EEG as recorded and in the right is +the filtered EEG on the [8, 64] Hz frequency band. we see less noise and +normalized amplitude values in a natural EEG range. + + +```python + +elec = 6 # Oz channel + +x_label = "Time (ms)" +y_label = "Voltage (uV)" +# Create subplots +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) + +# Plot data on the first subplot +ax1.plot(time, X_raw[:, elec, 0, 0], "r-") +ax1.set_xlabel(x_label) +ax1.set_ylabel(y_label) +ax1.set_title("Raw EEG : 1 second at Oz ") + +# Plot data on the second subplot +ax2.plot(time, X[0, :, elec, 0], "b-") +ax2.set_xlabel(x_label) +ax2.set_ylabel(y_label) +ax2.set_title("Filtered EEG between 8-64 Hz: 1 second at Oz") + +# Adjust spacing between subplots +plt.tight_layout() + +# Show the plot +plt.show() +``` + + + +![png](/img/examples/timeseries/eeg_bci_ssvepformer/eeg_bci_ssvepformer_19_0.png) + + + +--- +## EEG frequency representation + +Using the welch method, we visualize the frequency power for a well performing subject +for the entire 4 seconds EEG recording at Oz electrode for each stimuli. the red peaks +indicate the stimuli fundamental frequency and the 2nd harmonics (double the fundamental +frequency). we see clear peaks showing the high responses from that subject which means +that this subject is a good candidate for SSVEP BCI control. In many cases the peaks +are weak or absent, meaning that subject do not achieve the task correctly. + +![eeg_frequency](/img/eeg_bci_ssvepformer/eeg_ssvepformer_frequencypowers.png) + +# Create Layers and model + +Create Layers in a cross-framework custom component fashion. +In the SSVEPFormer, the data is first transformed to the frequency domain through +Fast-Fourier transform (FFT), to construct a complex spectrum presentation consisting of +the concatenation of frequency and phase information in a fixed frequency band. To keep +the model in an end-to-end format, we implement the complex spectrum transformation as +non-trainable layer. + +![model](/img/eeg_bci_ssvepformer/eeg_ssvepformer_model.jpg) +The SSVEPFormer unlike the Transformer architecture does not contain positional encoding/embedding +layers which replaced a channel combination block that has a layer of Conv1D layer of 1 +kernel size with double input channels (double the count of electrodes) number of filters, +and LayerNorm, Gelu activation and dropout. +Another difference with Transformers is the absence of multi-head attention layers with +attention mechanism. +The model encoder contains two identical and successive blocks. Each block has two +sub-blocks of CNN module and MLP module. the CNN module consists of a LayerNorm, Conv1D +with the same number of filters as channel combination, LayerNorm, Gelu, Dropout and an +residual connection. The MLP module consists of a LayerNorm, Dense layer, Gelu, droput +and residual connection. the Dense layer is applied on each channel separately. +The last block of the model is MLP head with Flatten layer, Dropout, Dense, LayerNorm, +Gelu, Dropout and Dense layer with softmax acitvation. +All trainable weights are initialized by a normal distribution with 0 mean and 0.01 +standard deviation as state in the original paper. + + +```python + +class ComplexSpectrum(keras.layers.Layer): + def __init__(self, nfft=512, fft_start=8, fft_end=64): + super().__init__() + self.nfft = nfft + self.fft_start = fft_start + self.fft_end = fft_end + + def call(self, x): + samples = x.shape[-1] + x = keras.ops.rfft(x, fft_length=self.nfft) + real = x[0] / samples + imag = x[1] / samples + real = real[:, :, self.fft_start : self.fft_end] + imag = imag[:, :, self.fft_start : self.fft_end] + x = keras.ops.concatenate((real, imag), axis=-1) + return x + + +class ChannelComb(keras.layers.Layer): + def __init__(self, n_channels, drop_rate=0.5): + super().__init__() + self.conv = layers.Conv1D( + 2 * n_channels, + 1, + padding="same", + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.normalization = layers.LayerNormalization() + self.activation = layers.Activation(activation="gelu") + self.drop = layers.Dropout(drop_rate) + + def call(self, x): + x = self.conv(x) + x = self.normalization(x) + x = self.activation(x) + x = self.drop(x) + return x + + +class ConvAttention(keras.layers.Layer): + def __init__(self, n_channels, drop_rate=0.5): + super().__init__() + self.norm = layers.LayerNormalization() + self.conv = layers.Conv1D( + 2 * n_channels, + 31, + padding="same", + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.activation = layers.Activation(activation="gelu") + self.drop = layers.Dropout(drop_rate) + + def call(self, x): + input = x + x = self.norm(x) + x = self.conv(x) + x = self.activation(x) + x = self.drop(x) + x = x + input + return x + + +class ChannelMLP(keras.layers.Layer): + def __init__(self, n_features, drop_rate=0.5): + super().__init__() + self.norm = layers.LayerNormalization() + self.mlp = layers.Dense( + 2 * n_features, + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.activation = layers.Activation(activation="gelu") + self.drop = layers.Dropout(drop_rate) + self.cat = layers.Concatenate(axis=1) + + def call(self, x): + input = x + channels = x.shape[1] # x shape : NCF + x = self.norm(x) + output_channels = [] + for i in range(channels): + c = self.mlp(x[:, :, i]) + c = layers.Reshape([1, -1])(c) + output_channels.append(c) + x = self.cat(output_channels) + x = self.activation(x) + x = self.drop(x) + x = x + input + return x + + +class Encoder(keras.layers.Layer): + def __init__(self, n_channels, n_features, drop_rate=0.5): + super().__init__() + self.attention1 = ConvAttention(n_channels, drop_rate=drop_rate) + self.mlp1 = ChannelMLP(n_features, drop_rate=drop_rate) + self.attention2 = ConvAttention(n_channels, drop_rate=drop_rate) + self.mlp2 = ChannelMLP(n_features, drop_rate=drop_rate) + + def call(self, x): + x = self.attention1(x) + x = self.mlp1(x) + x = self.attention2(x) + x = self.mlp2(x) + return x + + +class MlpHead(keras.layers.Layer): + def __init__(self, n_classes, drop_rate=0.5): + super().__init__() + self.flatten = layers.Flatten() + self.drop = layers.Dropout(drop_rate) + self.linear1 = layers.Dense( + 6 * n_classes, + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + self.norm = layers.LayerNormalization() + self.activation = layers.Activation(activation="gelu") + self.drop2 = layers.Dropout(drop_rate) + self.linear2 = layers.Dense( + n_classes, + kernel_initializer=keras.initializers.RandomNormal( + mean=0.0, stddev=0.01, seed=None + ), + ) + + def call(self, x): + x = self.flatten(x) + x = self.drop(x) + x = self.linear1(x) + x = self.norm(x) + x = self.activation(x) + x = self.drop2(x) + x = self.linear2(x) + return x + +``` + +### Create a sequential model with the layers above + + +```python + +def create_ssvepformer( + input_shape, fs, resolution, fq_band, n_channels, n_classes, drop_rate +): + nfft = round(fs / resolution) + fft_start = int(fq_band[0] / resolution) + fft_end = int(fq_band[1] / resolution) + 1 + n_features = fft_end - fft_start + + model = keras.Sequential( + [ + keras.Input(shape=input_shape), + ComplexSpectrum(nfft, fft_start, fft_end), + ChannelComb(n_channels=n_channels, drop_rate=drop_rate), + Encoder(n_channels=n_channels, n_features=n_features, drop_rate=drop_rate), + Encoder(n_channels=n_channels, n_features=n_features, drop_rate=drop_rate), + MlpHead(n_classes=n_classes, drop_rate=drop_rate), + layers.Activation(activation="softmax"), + ] + ) + + return model + +``` + +# Evaluation + + +```python +# Training settings same as the original paper +BATCH_SIZE = 128 +EPOCHS = 100 +LR = 0.001 # learning rate +WD = 0.001 # weight decay +MOMENTUM = 0.9 +DROP_RATE = 0.5 + +resolution = 0.25 +``` + +From the entire dataset we select folds for each subject evaluation. +construct a tf dataset object for train and testing data and create the model and launch +the training using SGD optimizer. + + +```python + +def concatenate_subjects(x, y, fold): + X = np.concatenate([x[idx] for idx in fold], axis=-1) + Y = np.concatenate([y[idx] for idx in fold], axis=-1) + X = X.transpose((2, 1, 0)) # trials x channels x samples + return X, Y - 1 # transform labels to values from 0...11 + + +def evaluate_subject( + x_train, + y_train, + x_val, + y_val, + input_shape, + fs=256, + resolution=0.25, + band=[8, 64], + channels=8, + n_classes=12, + drop_rate=DROP_RATE, +): + + train_dataset = ( + tf.data.Dataset.from_tensor_slices((x_train, y_train)) + .batch(BATCH_SIZE) + .prefetch(tf.data.AUTOTUNE) + ) + + test_dataset = ( + tf.data.Dataset.from_tensor_slices((x_val, y_val)) + .batch(BATCH_SIZE) + .prefetch(tf.data.AUTOTUNE) + ) + + model = create_ssvepformer( + input_shape, fs, resolution, band, channels, n_classes, drop_rate + ) + sgd = keras.optimizers.SGD(learning_rate=LR, momentum=MOMENTUM, weight_decay=WD) + + model.compile( + loss="sparse_categorical_crossentropy", + optimizer=sgd, + metrics=["accuracy"], + jit_compile=True, + ) + + history = model.fit( + train_dataset, + batch_size=BATCH_SIZE, + epochs=EPOCHS, + validation_data=test_dataset, + verbose=0, + ) + loss, acc = model.evaluate(test_dataset) + return acc * 100 + +``` + +--- +## Run evaluation + + +```python +channels = X.shape[2] +samples = X.shape[1] +input_shape = (channels, samples) +n_classes = 12 + +model = create_ssvepformer( + input_shape, fs, resolution, band, channels, n_classes, DROP_RATE +) +model.summary() +``` + + +
Model: "sequential"
+
+ + + + +
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
+┃ Layer (type)                          Output Shape                         Param # ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
+│ complex_spectrum (ComplexSpectrum)   │ (None, 8, 450)              │               0 │
+├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
+│ channel_comb (ChannelComb)           │ (None, 16, 450)             │           1,044 │
+├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
+│ encoder (Encoder)                    │ (None, 16, 450)             │          34,804 │
+├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
+│ encoder_1 (Encoder)                  │ (None, 16, 450)             │          34,804 │
+├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
+│ mlp_head (MlpHead)                   │ (None, 12)                  │         519,492 │
+├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
+│ activation_10 (Activation)           │ (None, 12)                  │               0 │
+└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
+
+ + + + +
 Total params: 590,144 (2.25 MB)
+
+ + + + +
 Trainable params: 590,144 (2.25 MB)
+
+ + + + +
 Non-trainable params: 0 (0.00 B)
+
+ + + +--- +## Evaluation on all subjects following a leave-one-subject out data repartition scheme + + +```python +accs = np.zeros(10) + +for subject in range(10): + print(f"Testing subject: {subject+ 1}") + + # create train / test folds + folds = np.delete(np.arange(10), subject) + train_index = folds + test_index = [subject] + + # create data split for each subject + x_train, y_train = concatenate_subjects(X, Y, train_index) + x_val, y_val = concatenate_subjects(X, Y, test_index) + + # train and evaluate a fold and compute the time it takes + acc = evaluate_subject(x_train, y_train, x_val, y_val, input_shape) + + accs[subject] = acc + +print(f"\nAccuracy Across Subjects: {accs.mean()} % std: {np.std(accs)}") +``` + +
+``` +Testing subject: 1 + +WARNING: All log messages before absl::InitializeLog() is called are written to STDERR +I0000 00:00:1737801392.665434 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.156241 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.156492 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.160002 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.160279 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.160421 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.168480 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.168667 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 +I0000 00:00:1737801393.168908 1425 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5938 - loss: 1.2483 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - accuracy: 0.5868 - loss: 1.3010 + + +
+``` +Testing subject: 2 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5469 - loss: 1.5270 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5119 - loss: 1.5974 + + +
+``` +Testing subject: 3 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7266 - loss: 0.8867 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6903 - loss: 1.0117 + + +
+``` +Testing subject: 4 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.1574 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9637 - loss: 0.1487 + + +
+``` +Testing subject: 5 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9375 - loss: 0.2184 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9458 - loss: 0.1887 + + +
+``` +Testing subject: 6 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9688 - loss: 0.1117 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9674 - loss: 0.1018 + + +
+``` +Testing subject: 7 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9141 - loss: 0.2639 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9158 - loss: 0.2592 + + +
+``` +Testing subject: 8 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9922 - loss: 0.0562 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9937 - loss: 0.0518 + + +
+``` +Testing subject: 9 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0669 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9837 - loss: 0.0701 + + +
+``` +Testing subject: 10 + +``` +
+ + 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9219 - loss: 0.3438 + +
+``` + +``` +
+ 2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.8999 - loss: 0.4543 + + + +
+``` +Accuracy Across Subjects: 84.11111384630203 % std: 17.575586372993953 + +``` +
+and that's it! we see how some subjects with no data on the training set still can achieve +almost a 100% correct commands and others show poor performance around 50%. In the original +paper using PyTorch the average accuracy was 84.04% with 17.37 std. we reached the same +values knowing the stochastic nature of deep learning. + +# Visualizations + +Training and inference times comparison between the different backends (Jax, Tensorflow +and PyTorch) on the three GPUs available with Colab Free/Pro/Pro+: T4, L4, A100. + + +--- +## Training Time + +![training_time](/img/eeg_bci_ssvepformer/eeg_ssvepformer_keras_training_time.png) + +# Inference Time + +![inference_time](/img/eeg_bci_ssvepformer/eeg_ssvepformer_keras_inference_time.png) + +the Jax backend was the best on training and inference in all the GPUs, the PyTorch was +exremely slow due to the jit compilation option being disable because of the complex +data type calculated by FFT which is not supported by the PyTorch jit compiler. + +# Acknowledgment + +I thank Chris Perry [X](https://x.com/thechrisperry) @GoogleColab for supporting this +work with GPU compute. + +# References +[1] Chen, J. et al. (2023) ‘A transformer-based deep neural network model for SSVEP +classification’, Neural Networks, 164, pp. 521–534. Available at: https://doi.org/10.1016/j.neunet.2023.04.045. + +[2] Nakanishi, M. et al. (2015) ‘A Comparison Study of Canonical Correlation Analysis +Based Methods for Detecting Steady-State Visual Evoked Potentials’, Plos One, 10(10), p. +e0140703. Available at: https://doi.org/10.1371/journal.pone.0140703 From a41e579207fdbbc1f4a35cfc3b0799c6191b5bde Mon Sep 17 00:00:00 2001 From: Okba BEKHELIFI Date: Tue, 28 Jan 2025 18:29:17 +0100 Subject: [PATCH 4/5] - Edited the .md file after review. On branch master Your branch is up to date with 'origin/master'. Changes to be committed: modified: examples/timeseries/md/eeg_bci_ssvepformer.md --- examples/timeseries/md/eeg_bci_ssvepformer.md | 29 +++++++------------ 1 file changed, 10 insertions(+), 19 deletions(-) diff --git a/examples/timeseries/md/eeg_bci_ssvepformer.md b/examples/timeseries/md/eeg_bci_ssvepformer.md index db364afa4e..27d60a0b7e 100644 --- a/examples/timeseries/md/eeg_bci_ssvepformer.md +++ b/examples/timeseries/md/eeg_bci_ssvepformer.md @@ -793,7 +793,7 @@ I0000 00:00:1737801393.168908 1425 cuda_executor.cc:1015] successful NUMA nod
``` - + ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - accuracy: 0.5868 - loss: 1.3010 @@ -806,11 +806,10 @@ Testing subject: 2 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5469 - loss: 1.5270 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5469 - loss: 1.5270
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5119 - loss: 1.5974 @@ -823,11 +822,10 @@ Testing subject: 3 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7266 - loss: 0.8867 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7266 - loss: 0.8867
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6903 - loss: 1.0117 @@ -840,11 +838,10 @@ Testing subject: 4 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.1574 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9688 - loss: 0.1574
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9637 - loss: 0.1487 @@ -857,11 +854,10 @@ Testing subject: 5 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9375 - loss: 0.2184 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9375 - loss: 0.2184
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9458 - loss: 0.1887 @@ -874,11 +870,10 @@ Testing subject: 6 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9688 - loss: 0.1117 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9688 - loss: 0.1117
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9674 - loss: 0.1018 @@ -891,11 +886,10 @@ Testing subject: 7 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9141 - loss: 0.2639 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9141 - loss: 0.2639
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9158 - loss: 0.2592 @@ -908,11 +902,10 @@ Testing subject: 8 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9922 - loss: 0.0562 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9922 - loss: 0.0562
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9937 - loss: 0.0518 @@ -925,11 +918,10 @@ Testing subject: 9 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0669 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9844 - loss: 0.0669
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9837 - loss: 0.0701 @@ -942,11 +934,10 @@ Testing subject: 10 ``` - 1/2 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9219 - loss: 0.3438 + 1/2 ━━━━━━━━━━[37m━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9219 - loss: 0.3438
``` - ```
2/2 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.8999 - loss: 0.4543 From 4deb98263f39d5019ea2c7bc97b6c87f5bdeb6bc Mon Sep 17 00:00:00 2001 From: Okba BEKHELIFI Date: Thu, 6 Feb 2025 10:14:01 +0100 Subject: [PATCH 5/5] - Added newline at the end of fileX On branch master Your branch is up to date with 'origin/master'. Changes to be committed: modified: examples/timeseries/eeg_bci_ssvepformer.py --- examples/timeseries/eeg_bci_ssvepformer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/timeseries/eeg_bci_ssvepformer.py b/examples/timeseries/eeg_bci_ssvepformer.py index 8d02003645..fe4d0ec4e4 100644 --- a/examples/timeseries/eeg_bci_ssvepformer.py +++ b/examples/timeseries/eeg_bci_ssvepformer.py @@ -652,4 +652,4 @@ def evaluate_subject( [2] Nakanishi, M. et al. (2015) ‘A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials’, Plos One, 10(10), p. e0140703. Available at: https://doi.org/10.1371/journal.pone.0140703 -""" \ No newline at end of file +"""

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Brain-Computer Interface\n", + "\n", + "**Author:** [Okba Bekhelifi](https://github.com/okbalefthanded)
\n", + "**Date created:** 2025/01/08
\n", + "**Last modified:** 2025/01/08
\n", + "**Description:** A Transformer based classification for EEG signal for BCI." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Introduction\n", + "\n", + "This tutorial will explain how to build a Transformer based Neural Network to classify\n", + "Brain-Computer Interface (BCI) Electroencephalograpy (EEG) data recorded in a\n", + "Steady-State Visual Evoked Potentials (SSVEPs) experiment for the application of a\n", + "brain-controlled speller.\n", + "\n", + "The tutorial reproduces an experiment from the SSVEPFormer study [1]\n", + "( [arXiv preprint](https://arxiv.org/abs/2210.04172) /\n", + "[Peer-Reviewed paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023002319) ).\n", + "This model was the first Transformer based model to be introduced for SSVEP data classification,\n", + "we will test it on the Nakanishi et al. [2] public dataset as dataset 1 from the paper.\n", + "\n", + "The process follows an inter-subject classification experiment. Given N subject data in\n", + "the dataset, the training data partition contains data from N-1 subject and the remaining\n", + "single subject data is used for testing. the training set does not contain any sample from\n", + "the testing subject. This way we construct a true subject-independent model. We keep the\n", + "same parameters and settings as the original paper in all processing operations from\n", + "preprocessing to training.\n", + "\n", + "\n", + "The tutorial begins with a quick BCI and dataset description then, we go through the\n", + "technicalities following these sections:\n", + "- Setup, and imports.\n", + "- Dataset download and extraction.\n", + "- Data preprocessing: EEG data filtering, segmentation and visualization of raw and\n", + "filtered data, and frequency response for a well performing participant.\n", + "- Layers and model creation.\n", + "- Evaluation: a single participant data classification as an example then the total\n", + "participants data classification.\n", + "- Visulization: we show the results of training and inference times comparison among\n", + "the Keras 3 available backends (JAX, Tensorflow, and PyTorch) on three different GPUs.\n", + "- Conclusion: final discussion and remarks." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Dataset description\n", + "\n", + "## BCI and SSVEP:\n", + "A BCI offers the ability to communicate using only brain activity, this can be achieved\n", + "through exogenous stimuli that generate specific responses indicating the intent of the\n", + "subject. the responses are elicited when the user focuses their attention on the target\n", + "stimulus. We can use visual stimuli by presenting the subject with a set of options\n", + "typically on a monitor as a grid to select one command at a time. Each stimulus will\n", + "flicker following a fixed frequency and phase, the resulting EEG recorded at occipital\n", + "and occipito-parietal areas of the cortex (visual cortex) will have higher power in the\n", + "associated frequency with the stimulus where the subject was looking at. This type of\n", + "BCI paradigm is called the Steady-State Visual Evoked Potentials (SSVEPs) and became\n", + "widely used for multiple application due to its reliability and high perfromance in\n", + "classification and rapidity as a 1-second of EEG is sufficient making a command. Other\n", + "types of brain responses exists and do not require external stimulations, however they\n", + "are less reliable.\n", + "[Demo video](https://www.youtube.com/watch?v=VtA6jsEMIug)\n", + "\n", + "This tutorials uses the 12 commands (class) public SSVEP dataset [2] with the following\n", + "interface emulating a phone dialing numbers.\n", + "![dataset](/img/eeg_bci_ssvepformer/eeg_ssvepformer_dataset1_interface.jpg)\n", + "\n", + "The dataset was recorded with 10 participants, each faced the above 12 SSVEP stimuli (A).\n", + "The stimulation frequencies ranged from 9.25Hz to 14.75 Hz with 0.5Hz step, and phases\n", + "ranged from 0 to 1.5 \u03c0 with 0.5 \u03c0 step for each row.(B). The EEG signal was acquired\n", + "with 8 electrodes (channels) (PO7, PO3, POz,\n", + "PO4, PO8, O1, Oz, O2) sampling frequency was 2048 Hz then the stored data were\n", + "downsampled to 256 Hz. The subjects completed 15 blocks of recordings, each consisted\n", + "of 12 random ordered stimulations (1 for each class) of 4 seconds each. In total,\n", + "each subject conducted 180 trials.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Select JAX backend" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"jax\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Install dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "!pip install -q numpy\n", + "!pip install -q scipy\n", + "!pip install -q matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Imports\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "# deep learning libraries\n", + "from keras import backend as K\n", + "from keras import layers\n", + "import keras\n", + "\n", + "# visualization and signal processing imports\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "from scipy.signal import butter, filtfilt\n", + "from scipy.io import loadmat\n", + "\n", + "# setting the backend, seed and Keras channel format\n", + "K.set_image_data_format(\"channels_first\")\n", + "keras.utils.set_random_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Download and extract dataset\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Nakanishi et. al 2015 [DataSet Repo](https://github.com/mnakanishi/12JFPM_SSVEP)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "!curl -O https://sccn.ucsd.edu/download/cca_ssvep.zip\n", + "!unzip cca_ssvep.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Pre-Processing\n", + "\n", + "The preprocessing steps followed are first to read the EEG data for each subject, then\n", + "to filter the raw data in a frequency interval where most useful information lies,\n", + "then we select a fixed duration of signal starting from the onset of the stimulation\n", + "(due to latency delay caused by the visual system we start we add 135 milliseconds to\n", + "the stimulation onset). Lastly, all subjects data are concatenated in a single Tensor\n", + "of the shape: [subjects x samples x channels x trials]. The data labels are also\n", + "concatenated following the order of the trials in the experiments and will be a\n", + "matrix of the shape [subjects x trials]\n", + "(here by channels we mean electrodes, we use this notation throughout the tutorial)." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "\n", + "def raw_signal(folder, fs=256, duration=1.0, onset=0.135):\n", + " \"\"\"selecting a 1-second segment of the raw EEG signal for\n", + " subject 1.\n", + " \"\"\"\n", + " onset = 38 + int(onset * fs)\n", + " end = int(duration * fs)\n", + " data = loadmat(f\"{folder}/s1.mat\")\n", + " # samples, channels, trials, targets\n", + " eeg = data[\"eeg\"].transpose((2, 1, 3, 0))\n", + " # segment data\n", + " eeg = eeg[onset : onset + end, :, :, :]\n", + " return eeg\n", + "\n", + "\n", + "def segment_eeg(\n", + " folder, elecs=None, fs=256, duration=1.0, band=[5.0, 45.0], order=4, onset=0.135\n", + "):\n", + " \"\"\"Filtering and segmenting EEG signals for all subjects.\"\"\"\n", + " n_subejects = 10\n", + " onset = 38 + int(onset * fs)\n", + " end = int(duration * fs)\n", + " X, Y = [], [] # empty data and labels\n", + "\n", + " for subj in range(1, n_subejects + 1):\n", + " data = loadmat(f\"{data_folder}/s{subj}.mat\")\n", + " # samples, channels, trials, targets\n", + " eeg = data[\"eeg\"].transpose((2, 1, 3, 0))\n", + " # filter data\n", + " eeg = filter_eeg(eeg, fs=fs, band=band, order=order)\n", + " # segment data\n", + " eeg = eeg[onset : onset + end, :, :, :]\n", + " # reshape labels\n", + " samples, channels, blocks, targets = eeg.shape\n", + " y = np.tile(np.arange(1, targets + 1), (blocks, 1))\n", + " y = y.reshape((1, blocks * targets), order=\"F\")\n", + "\n", + " X.append(eeg.reshape((samples, channels, blocks * targets), order=\"F\"))\n", + " Y.append(y)\n", + "\n", + " X = np.array(X, dtype=np.float32, order=\"F\")\n", + " Y = np.array(Y, dtype=np.float32).squeeze()\n", + "\n", + " return X, Y\n", + "\n", + "\n", + "def filter_eeg(data, fs=256, band=[5.0, 45.0], order=4):\n", + " \"\"\"Filter EEG signal using a zero-phase IIR filter\"\"\"\n", + " B, A = butter(order, np.array(band) / (fs / 2), btype=\"bandpass\")\n", + " return filtfilt(B, A, data, axis=0)\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Segment data into epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "data_folder = os.path.abspath(\"./cca_ssvep\")\n", + "band = [8, 64] # low-frequency / high-frequency cutoffS\n", + "order = 4 # filter order\n", + "fs = 256 # sampling frequency\n", + "duration = 1.0 # 1 second\n", + "\n", + "# raw signal\n", + "X_raw = raw_signal(data_folder, fs=fs, duration=duration)\n", + "print(\n", + " f\"A single subject raw EEG (X_raw) shape: {X_raw.shape} [Samples x Channels x Blocks x Targets]\"\n", + ")\n", + "\n", + "# segmented signal\n", + "X, Y = segment_eeg(data_folder, band=band, order=order, fs=fs, duration=duration)\n", + "print(\n", + " f\"Full training data (X) shape: {X.shape} [Subject x Samples x Channels x Trials]\"\n", + ")\n", + "print(f\"data labels (Y) shape: {Y.shape} [Subject x Trials]\")\n", + "\n", + "samples = X.shape[1]\n", + "time = np.linspace(0.0, samples / fs, samples) * 1000" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Visualize EEG signal" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## EEG in time\n", + "\n", + "Raw EEG vs Filtered EEG\n", + "The same 1-second recording for subject s1 at Oz (central electrode in the visual cortex,\n", + "back of the head) is illustrated. left is the raw EEG as recorded and in the right is\n", + "the filtered EEG on the [8, 64] Hz frequency band. we see less noise and\n", + "normalized amplitude values in a natural EEG range." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "\n", + "elec = 6 # Oz channel\n", + "\n", + "x_label = \"Time (ms)\"\n", + "y_label = \"Voltage (uV)\"\n", + "# Create subplots\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "# Plot data on the first subplot\n", + "ax1.plot(time, X_raw[:, elec, 0, 0], \"r-\")\n", + "ax1.set_xlabel(x_label)\n", + "ax1.set_ylabel(y_label)\n", + "ax1.set_title(\"Raw EEG : 1 second at Oz \")\n", + "\n", + "# Plot data on the second subplot\n", + "ax2.plot(time, X[0, :, elec, 0], \"b-\")\n", + "ax2.set_xlabel(x_label)\n", + "ax2.set_ylabel(y_label)\n", + "ax2.set_title(\"Filtered EEG between 8-64 Hz: 1 second at Oz\")\n", + "\n", + "# Adjust spacing between subplots\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## EEG frequency representation\n", + "\n", + "Using the welch method, we visualize the frequency power for a well performing subject\n", + "for the entire 4 seconds EEG recording at Oz electrode for each stimuli. the red peaks\n", + "indicate the stimuli fundamental frequency and the 2nd harmonics (double the fundamental\n", + "frequency). we see clear peaks showing the high responses from that subject which means\n", + "that this subject is a good candidate for SSVEP BCI control. In many cases the peaks\n", + "are weak or absent, meaning that subject do not achieve the task correctly.\n", + "\n", + "![eeg_frequency](/img/eeg_bci_ssvepformer/eeg_ssvepformer_frequencypowers.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Create Layers and model\n", + "\n", + "Create Layers in a cross-framework custom component fashion.\n", + "In the SSVEPFormer, the data is first transformed to the frequency domain through\n", + "Fast-Fourier transform (FFT), to construct a complex spectrum presentation consisting of\n", + "the concatenation of frequency and phase information in a fixed frequency band. To keep\n", + "the model in an end-to-end format, we implement the complex spectrum transformation as\n", + "non-trainable layer.\n", + "\n", + "![model](/img/eeg_bci_ssvepformer/eeg_ssvepformer_model.jpg)\n", + "The SSVEPFormer unlike the Transformer architecture does not contain positional encoding/embedding\n", + "layers which replaced a channel combination block that has a layer of Conv1D layer of 1\n", + "kernel size with double input channels (double the count of electrodes) number of filters,\n", + "and LayerNorm, Gelu activation and dropout.\n", + "Another difference with Transformers is the absence of multi-head attention layers with\n", + "attention mechanism.\n", + "The model encoder contains two identical and successive blocks. Each block has two\n", + "sub-blocks of CNN module and MLP module. the CNN module consists of a LayerNorm, Conv1D\n", + "with the same number of filters as channel combination, LayerNorm, Gelu, Dropout and an\n", + "residual connection. The MLP module consists of a LayerNorm, Dense layer, Gelu, droput\n", + "and residual connection. the Dense layer is applied on each channel separately.\n", + "The last block of the model is MLP head with Flatten layer, Dropout, Dense, LayerNorm,\n", + "Gelu, Dropout and Dense layer with softmax acitvation.\n", + "All trainable weights are initialized by a normal distribution with 0 mean and 0.01\n", + "standard deviation as state in the original paper." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "\n", + "class ComplexSpectrum(keras.layers.Layer):\n", + " def __init__(self, nfft=512, fft_start=8, fft_end=64):\n", + " super().__init__()\n", + " self.nfft = nfft\n", + " self.fft_start = fft_start\n", + " self.fft_end = fft_end\n", + "\n", + " def call(self, x):\n", + " samples = x.shape[-1]\n", + " x = keras.ops.rfft(x, fft_length=self.nfft)\n", + " real = x[0] / samples\n", + " imag = x[1] / samples\n", + " real = real[:, :, self.fft_start : self.fft_end]\n", + " imag = imag[:, :, self.fft_start : self.fft_end]\n", + " x = keras.ops.concatenate((real, imag), axis=-1)\n", + " return x\n", + "\n", + "\n", + "class ChannelComb(keras.layers.Layer):\n", + " def __init__(self, n_channels, drop_rate=0.5):\n", + " super().__init__()\n", + " self.conv = layers.Conv1D(\n", + " 2 * n_channels,\n", + " 1,\n", + " padding=\"same\",\n", + " kernel_initializer=keras.initializers.RandomNormal(\n", + " mean=0.0, stddev=0.01, seed=None\n", + " ),\n", + " )\n", + " self.normalization = layers.LayerNormalization()\n", + " self.activation = layers.Activation(activation=\"gelu\")\n", + " self.drop = layers.Dropout(drop_rate)\n", + "\n", + " def call(self, x):\n", + " x = self.conv(x)\n", + " x = self.normalization(x)\n", + " x = self.activation(x)\n", + " x = self.drop(x)\n", + " return x\n", + "\n", + "\n", + "class ConvAttention(keras.layers.Layer):\n", + " def __init__(self, n_channels, drop_rate=0.5):\n", + " super().__init__()\n", + " self.norm = layers.LayerNormalization()\n", + " self.conv = layers.Conv1D(\n", + " 2 * n_channels,\n", + " 31,\n", + " padding=\"same\",\n", + " kernel_initializer=keras.initializers.RandomNormal(\n", + " mean=0.0, stddev=0.01, seed=None\n", + " ),\n", + " )\n", + " self.activation = layers.Activation(activation=\"gelu\")\n", + " self.drop = layers.Dropout(drop_rate)\n", + "\n", + " def call(self, x):\n", + " input = x\n", + " x = self.norm(x)\n", + " x = self.conv(x)\n", + " x = self.activation(x)\n", + " x = self.drop(x)\n", + " x = x + input\n", + " return x\n", + "\n", + "\n", + "class ChannelMLP(keras.layers.Layer):\n", + " def __init__(self, n_features, drop_rate=0.5):\n", + " super().__init__()\n", + " self.norm = layers.LayerNormalization()\n", + " self.mlp = layers.Dense(\n", + " 2 * n_features,\n", + " kernel_initializer=keras.initializers.RandomNormal(\n", + " mean=0.0, stddev=0.01, seed=None\n", + " ),\n", + " )\n", + " self.activation = layers.Activation(activation=\"gelu\")\n", + " self.drop = layers.Dropout(drop_rate)\n", + " self.cat = layers.Concatenate(axis=1)\n", + "\n", + " def call(self, x):\n", + " input = x\n", + " channels = x.shape[1] # x shape : NCF\n", + " x = self.norm(x)\n", + " output_channels = []\n", + " for i in range(channels):\n", + " c = self.mlp(x[:, :, i])\n", + " c = layers.Reshape([1, -1])(c)\n", + " output_channels.append(c)\n", + " x = self.cat(output_channels)\n", + " x = self.activation(x)\n", + " x = self.drop(x)\n", + " x = x + input\n", + " return x\n", + "\n", + "\n", + "class Encoder(keras.layers.Layer):\n", + " def __init__(self, n_channels, n_features, drop_rate=0.5):\n", + " super().__init__()\n", + " self.attention1 = ConvAttention(n_channels, drop_rate=drop_rate)\n", + " self.mlp1 = ChannelMLP(n_features, drop_rate=drop_rate)\n", + " self.attention2 = ConvAttention(n_channels, drop_rate=drop_rate)\n", + " self.mlp2 = ChannelMLP(n_features, drop_rate=drop_rate)\n", + "\n", + " def call(self, x):\n", + " x = self.attention1(x)\n", + " x = self.mlp1(x)\n", + " x = self.attention2(x)\n", + " x = self.mlp2(x)\n", + " return x\n", + "\n", + "\n", + "class MlpHead(keras.layers.Layer):\n", + " def __init__(self, n_classes, drop_rate=0.5):\n", + " super().__init__()\n", + " self.flatten = layers.Flatten()\n", + " self.drop = layers.Dropout(drop_rate)\n", + " self.linear1 = layers.Dense(\n", + " 6 * n_classes,\n", + " kernel_initializer=keras.initializers.RandomNormal(\n", + " mean=0.0, stddev=0.01, seed=None\n", + " ),\n", + " )\n", + " self.norm = layers.LayerNormalization()\n", + " self.activation = layers.Activation(activation=\"gelu\")\n", + " self.drop2 = layers.Dropout(drop_rate)\n", + " self.linear2 = layers.Dense(\n", + " n_classes,\n", + " kernel_initializer=keras.initializers.RandomNormal(\n", + " mean=0.0, stddev=0.01, seed=None\n", + " ),\n", + " )\n", + "\n", + " def call(self, x):\n", + " x = self.flatten(x)\n", + " x = self.drop(x)\n", + " x = self.linear1(x)\n", + " x = self.norm(x)\n", + " x = self.activation(x)\n", + " x = self.drop2(x)\n", + " x = self.linear2(x)\n", + " return x\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "### Create a sequential model with the layers above" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "\n", + "def create_ssvepformer(\n", + " input_shape, fs, resolution, fq_band, n_channels, n_classes, drop_rate\n", + "):\n", + " nfft = round(fs / resolution)\n", + " fft_start = int(fq_band[0] / resolution)\n", + " fft_end = int(fq_band[1] / resolution) + 1\n", + " n_features = fft_end - fft_start\n", + "\n", + " model = keras.Sequential(\n", + " [\n", + " keras.Input(shape=input_shape),\n", + " ComplexSpectrum(nfft, fft_start, fft_end),\n", + " ChannelComb(n_channels=n_channels, drop_rate=drop_rate),\n", + " Encoder(n_channels=n_channels, n_features=n_features, drop_rate=drop_rate),\n", + " Encoder(n_channels=n_channels, n_features=n_features, drop_rate=drop_rate),\n", + " MlpHead(n_classes=n_classes, drop_rate=drop_rate),\n", + " layers.Activation(activation=\"softmax\"),\n", + " ]\n", + " )\n", + "\n", + " return model\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "# Training settings same as the original paper\n", + "BATCH_SIZE = 128\n", + "EPOCHS = 100\n", + "LR = 0.001 # learning rate\n", + "WD = 0.001 # weight decay\n", + "MOMENTUM = 0.9\n", + "DROP_RATE = 0.5\n", + "\n", + "resolution = 0.25" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "From the entire dataset we select folds for each subject evaluation.\n", + "construct a tf dataset object for train and testing data and create the model and launch\n", + "the training using SGD optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "\n", + "def concatenate_subjects(x, y, fold):\n", + " X = np.concatenate([x[idx] for idx in fold], axis=-1)\n", + " Y = np.concatenate([y[idx] for idx in fold], axis=-1)\n", + " X = X.transpose((2, 1, 0)) # trials x channels x samples\n", + " return X, Y - 1 # transform labels to values from 0...11\n", + "\n", + "\n", + "def evaluate_subject(\n", + " x_train,\n", + " y_train,\n", + " x_val,\n", + " y_val,\n", + " input_shape,\n", + " fs=256,\n", + " resolution=0.25,\n", + " band=[8, 64],\n", + " channels=8,\n", + " n_classes=12,\n", + " drop_rate=DROP_RATE,\n", + "):\n", + "\n", + " train_dataset = (\n", + " tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + " .batch(BATCH_SIZE)\n", + " .prefetch(tf.data.AUTOTUNE)\n", + " )\n", + "\n", + " test_dataset = (\n", + " tf.data.Dataset.from_tensor_slices((x_val, y_val))\n", + " .batch(BATCH_SIZE)\n", + " .prefetch(tf.data.AUTOTUNE)\n", + " )\n", + "\n", + " model = create_ssvepformer(\n", + " input_shape, fs, resolution, band, channels, n_classes, drop_rate\n", + " )\n", + " sgd = keras.optimizers.SGD(learning_rate=LR, momentum=MOMENTUM, weight_decay=WD)\n", + "\n", + " model.compile(\n", + " loss=\"sparse_categorical_crossentropy\",\n", + " optimizer=sgd,\n", + " metrics=[\"accuracy\"],\n", + " jit_compile=True,\n", + " )\n", + "\n", + " history = model.fit(\n", + " train_dataset,\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=test_dataset,\n", + " verbose=0,\n", + " )\n", + " loss, acc = model.evaluate(test_dataset)\n", + " return acc * 100\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Run evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "channels = X.shape[2]\n", + "samples = X.shape[1]\n", + "input_shape = (channels, samples)\n", + "n_classes = 12\n", + "\n", + "model = create_ssvepformer(\n", + " input_shape, fs, resolution, band, channels, n_classes, DROP_RATE\n", + ")\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Evaluation on all subjects following a leave-one-subject out data repartition scheme" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab_type": "code" + }, + "outputs": [], + "source": [ + "accs = np.zeros(10)\n", + "\n", + "for subject in range(10):\n", + " print(f\"Testing subject: {subject+ 1}\")\n", + "\n", + " # create train / test folds\n", + " folds = np.delete(np.arange(10), subject)\n", + " train_index = folds\n", + " test_index = [subject]\n", + "\n", + " # create data split for each subject\n", + " x_train, y_train = concatenate_subjects(X, Y, train_index)\n", + " x_val, y_val = concatenate_subjects(X, Y, test_index)\n", + "\n", + " # train and evaluate a fold and compute the time it takes\n", + " acc = evaluate_subject(x_train, y_train, x_val, y_val, input_shape)\n", + "\n", + " accs[subject] = acc\n", + "\n", + "print(f\"\\nAccuracy Across Subjects: {accs.mean()} % std: {np.std(accs)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "and that's it! we see how some subjects with no data on the training set still can achieve\n", + "almost a 100% correct commands and others show poor performance around 50%. In the original\n", + "paper using PyTorch the average accuracy was 84.04% with 17.37 std. we reached the same\n", + "values knowing the stochastic nature of deep learning." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Visualizations\n", + "\n", + "Training and inference times comparison between the different backends (Jax, Tensorflow\n", + "and PyTorch) on the three GPUs available with Colab Free/Pro/Pro+: T4, L4, A100.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "## Training Time\n", + "\n", + "![training_time](/img/eeg_bci_ssvepformer/eeg_ssvepformer_keras_training_time.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Inference Time\n", + "\n", + "![inference_time](/img/eeg_bci_ssvepformer/eeg_ssvepformer_keras_inference_time.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "the Jax backend was the best on training and inference in all the GPUs, the PyTorch was\n", + "exremely slow due to the jit compilation option being disable because of the complex\n", + "data type calculated by FFT which is not supported by the PyTorch jit compiler." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# Acknowledgment\n", + "\n", + "I thank Chris Perry [X](https://x.com/thechrisperry) @GoogleColab for supporting this\n", + "work with GPU compute." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text" + }, + "source": [ + "# References\n", + "[1] Chen, J. et al. (2023) \u2018A transformer-based deep neural network model for SSVEP\n", + "classification\u2019, Neural Networks, 164, pp. 521\u2013534. Available at: https://doi.org/10.1016/j.neunet.2023.04.045.\n", + "\n", + "[2] Nakanishi, M. et al. (2015) \u2018A Comparison Study of Canonical Correlation Analysis\n", + "Based Methods for Detecting Steady-State Visual Evoked Potentials\u2019, Plos One, 10(10), p.\n", + "e0140703. Available at: https://doi.org/10.1371/journal.pone.0140703" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "eeg_bci_ssvepformer", + "private_outputs": false, + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/timeseries/md/eeg_bci_ssvepformer.md b/examples/timeseries/md/eeg_bci_ssvepformer.md new file mode 100644 index 0000000000..db364afa4e --- /dev/null +++ b/examples/timeseries/md/eeg_bci_ssvepformer.md @@ -0,0 +1,997 @@ +# Electroencephalogram Signal Classification for Brain-Computer Interface + +**Author:** [Okba Bekhelifi](https://github.com/okbalefthanded)
+**Date created:** 2025/01/08
+**Last modified:** 2025/01/08
+**Description:** A Transformer based classification for EEG signal for BCI. + + + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/timeseries/ipynb/eeg_bci_ssvepformer.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/timeseries/eeg_bci_ssvepformer.py) + + + +# Introduction + +This tutorial will explain how to build a Transformer based Neural Network to classify +Brain-Computer Interface (BCI) Electroencephalograpy (EEG) data recorded in a +Steady-State Visual Evoked Potentials (SSVEPs) experiment for the application of a +brain-controlled speller. + +The tutorial reproduces an experiment from the SSVEPFormer study [1] +( [arXiv preprint](https://arxiv.org/abs/2210.04172) / +[Peer-Reviewed paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023002319) ). +This model was the first Transformer based model to be introduced for SSVEP data classification, +we will test it on the Nakanishi et al. [2] public dataset as dataset 1 from the paper. + +The process follows an inter-subject classification experiment. Given N subject data in +the dataset, the training data partition contains data from N-1 subject and the remaining +single subject data is used for testing. the training set does not contain any sample from +the testing subject. This way we construct a true subject-independent model. We keep the +same parameters and settings as the original paper in all processing operations from +preprocessing to training. + + +The tutorial begins with a quick BCI and dataset description then, we go through the +technicalities following these sections: +- Setup, and imports. +- Dataset download and extraction. +- Data preprocessing: EEG data filtering, segmentation and visualization of raw and +filtered data, and frequency response for a well performing participant. +- Layers and model creation. +- Evaluation: a single participant data classification as an example then the total +participants data classification. +- Visulization: we show the results of training and inference times comparison among +the Keras 3 available backends (JAX, Tensorflow, and PyTorch) on three different GPUs. +- Conclusion: final discussion and remarks. + +# Dataset description + +--- +## BCI and SSVEP: +A BCI offers the ability to communicate using only brain activity, this can be achieved +through exogenous stimuli that generate specific responses indicating the intent of the +subject. the responses are elicited when the user focuses their attention on the target +stimulus. We can use visual stimuli by presenting the subject with a set of options +typically on a monitor as a grid to select one command at a time. Each stimulus will +flicker following a fixed frequency and phase, the resulting EEG recorded at occipital +and occipito-parietal areas of the cortex (visual cortex) will have higher power in the +associated frequency with the stimulus where the subject was looking at. This type of +BCI paradigm is called the Steady-State Visual Evoked Potentials (SSVEPs) and became +widely used for multiple application due to its reliability and high perfromance in +classification and rapidity as a 1-second of EEG is sufficient making a command. Other +types of brain responses exists and do not require external stimulations, however they +are less reliable. +[Demo video](https://www.youtube.com/watch?v=VtA6jsEMIug) + +This tutorials uses the 12 commands (class) public SSVEP dataset [2] with the following +interface emulating a phone dialing numbers. +![dataset](/img/eeg_bci_ssvepformer/eeg_ssvepformer_dataset1_interface.jpg) + +The dataset was recorded with 10 participants, each faced the above 12 SSVEP stimuli (A). +The stimulation frequencies ranged from 9.25Hz to 14.75 Hz with 0.5Hz step, and phases +ranged from 0 to 1.5 π with 0.5 π step for each row.(B). The EEG signal was acquired +with 8 electrodes (channels) (PO7, PO3, POz, +PO4, PO8, O1, Oz, O2) sampling frequency was 2048 Hz then the stored data were +downsampled to 256 Hz. The subjects completed 15 blocks of recordings, each consisted +of 12 random ordered stimulations (1 for each class) of 4 seconds each. In total, +each subject conducted 180 trials. + + +# Setup + +--- +## Select JAX backend + + +```python +import os + +os.environ["KERAS_BACKEND"] = "jax" +``` + +--- +## Install dependencies + + +```python +!pip install -q numpy +!pip install -q scipy +!pip install -q matplotlib +``` + +# Imports + + + +```python +# deep learning libraries +from keras import backend as K +from keras import layers +import keras + +# visualization and signal processing imports +import matplotlib.pyplot as plt +import tensorflow as tf +import numpy as np +from scipy.signal import butter, filtfilt +from scipy.io import loadmat + +# setting the backend, seed and Keras channel format +K.set_image_data_format("channels_first") +keras.utils.set_random_seed(42) +``` + +# Download and extract dataset + + +--- +## Nakanishi et. al 2015 [DataSet Repo](https://github.com/mnakanishi/12JFPM_SSVEP) + + +```python +!curl -O https://sccn.ucsd.edu/download/cca_ssvep.zip +!unzip cca_ssvep.zip +``` + +

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