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braindecode.models.SincShallowNet#

+
+
+class braindecode.models.SincShallowNet(num_time_filters: int = 32, time_filter_len: int = 33, depth_multiplier: int = 2, activation: ~torch.nn.modules.module.Module | None = <class 'torch.nn.modules.activation.ELU'>, drop_prob: float = 0.5, first_freq: float = 5.0, min_freq: float = 1.0, freq_stride: float = 1.0, padding: str = 'same', bandwidth: float = 4.0, pool_size: int = 55, pool_stride: int = 12, n_chans: int | None = None, n_outputs: int | None = None, n_times: int | None = None, input_window_seconds: float | None = None, sfreq: float | None = None, chs_info: ~typing.List[~typing.Dict] | None = None)[source]#
+

Sinc-ShallowNet from Borra, D et al (2020) [borra2020].

+
+SincShallowNet Architecture +
+

The Sinc-ShallowNet architecture has these fundamental blocks:

+
    +
  1. +
    Block 1: Spectral and Spatial Feature Extraction
      +
    • +
      Temporal Sinc-Convolutional Layer:

      Uses parametrized sinc functions to learn band-pass filters, +significantly reducing the number of trainable parameters by only +learning the lower and upper cutoff frequencies for each filter.

      +
      +
      +
      • +
    +
    +
    +
      +
    • +
      Spatial Depthwise Convolutional Layer:

      Applies depthwise convolutions to learn spatial filters for +each temporal feature map independently, further reducing +parameters and enhancing interpretability.

      +
      +
      +
      • +

    • Batch Normalization

      • +
    +
    2. +
  2. +
    Block 2: Temporal Aggregation
      +

    • Activation Function: ELU

      • +

    • Average Pooling Layer: Aggregation by averaging spatial dim

      • +

    • Dropout Layer

      • +

    • Flatten Layer

      • +
    +
    +
    +
    3. +
  3. +
    Block 3: Classification
      +

    • Fully Connected Layer: Maps the feature vector to n_outputs.

      • +
    +
    +
    +
    4. +
+

Implementation Notes:

+
    +
  • +
    The sinc-convolutional layer initializes cutoff frequencies uniformly

    within the desired frequency range and updates them during training while +ensuring the lower cutoff is less than the upper cutoff.

    +
    +
    +
    • +
+
+
Parameters:
+
    +

  • num_time_filters (int) – Number of temporal filters in the SincFilter layer.

    • +

  • time_filter_len (int) – Size of the temporal filters.

    • +

  • depth_multiplier (int) – Depth multiplier for spatial filtering.

    • +

  • activation (nn.Module, optional) – Activation function to use. Default is nn.ELU().

    • +

  • drop_prob (float, optional) – Dropout probability. Default is 0.5.

    • +

  • first_freq (float, optional) – The starting frequency for the first Sinc filter. Default is 5.0.

    • +

  • min_freq (float, optional) – Minimum frequency allowed for the low frequencies of the filters. Default is 1.0.

    • +

  • freq_stride (float, optional) – Frequency stride for the Sinc filters. Controls the spacing between the filter frequencies. +Default is 1.0.

    • +

  • padding (str, optional) – Padding mode for convolution, either ‘same’ or ‘valid’. Default is ‘same’.

    • +

  • bandwidth (float, optional) – Initial bandwidth for each Sinc filter. Default is 4.0.

    • +

  • pool_size (int, optional) – Size of the pooling window for the average pooling layer. Default is 55.

    • +

  • pool_stride (int, optional) – Stride of the pooling operation. Default is 12.

    • +

  • n_chans (int) – Number of EEG channels.

    • +

  • n_outputs (int) – Number of outputs of the model. This is the number of classes +in the case of classification.

    • +

  • n_times (int) – Number of time samples of the input window.

    • +

  • input_window_seconds (float) – Length of the input window in seconds.

    • +

  • sfreq (float) – Sampling frequency of the EEG recordings.

    • +

  • chs_info (list of dict) – Information about each individual EEG channel. This should be filled with +info["chs"]. Refer to mne.Info for more details.

    • +
+
+
Raises:
+
    +

  • ValueError – If some input signal-related parameters are not specified: and can not be inferred.

    • +

  • FutureWarning – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.

    • +
+
+
+

Notes

+

This implementation is based on the implementation from [sincshallowcode].

+

References

+
+
+[borra2020] +

Borra, D., Fantozzi, S., & Magosso, E. (2020). Interpretable +and lightweight convolutional neural network for EEG decoding: Application +to movement execution and imagination. Neural Networks, 129, 55-74.

+
+
+[sincshallowcode] +

Sinc-ShallowNet re-implementation source code: +marcellosicbaldi/SincNet-Tensorflow

+
+
+

Methods

+
+
+forward(x: Tensor) → Tensor[source]#
+

Forward pass of the model.

+
+
Parameters:
+

x (torch.Tensor) – Input tensor of shape [batch_size, num_channels, num_samples].

+
+
Returns:
+

Output logits of shape [batch_size, num_classes].

+
+
Return type:
+

torch.Tensor

+
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+ +
+ +
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+ + + + + + + +
+ + + + +
+ + +
+
+ On this page +
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