braindecode.models.SincShallowNet#

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

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

Convolution Interpretability

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. Block 2: Temporal Aggregation

    • Activation Function: ELU

    • Average Pooling Layer: Aggregation by averaging spatial dim

    • Dropout Layer

    • Flatten Layer

  3. Block 3: Classification

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

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 (type[Module] | None) – Activation function to use. Default is nn.ELU().

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

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

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

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

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

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

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

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

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)[source]#

Forward pass of the model.

Parameters:

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

Returns:

Output logits of shape [batch_size, num_classes].

Return type:

Tensor