braindecode.models.SCCNet#

class braindecode.models.SCCNet(n_chans=None, n_outputs=None, n_times=None, chs_info=None, input_window_seconds=None, sfreq=None, n_spatial_filters: int = 22, n_spatial_filters_smooth: int = 20, drop_prob: float = 0.5, activation: ~torch.nn.modules.module.Module = <class 'braindecode.models.modules.LogActivation'>)[source]#

SCCNet from Wei, C S (2019) [sccnet].

Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification.

Spatial component-wise convolutional network
  1. Spatial Component Analysis: Performs convolution spatial filtering

    across all EEG channels to extract spatial components, effectively reducing the channel dimension.

  2. Spatio-Temporal Filtering: Applies convolution across the spatial

    components and temporal domain to capture spatio-temporal patterns.

  3. Temporal Smoothing (Pooling): Uses average pooling over time to smooth the features and reduce the temporal dimension, focusing on longer-term patterns.

  4. Classification: Flattens the features and applies a fully connected layer.

Parameters:

  • 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.

  • 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.

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

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

  • n_spatial_filters (int, optional) – Number of spatial filters in the first convolutional layer. Default is 22.

  • n_spatial_filters_smooth (int, optional) – Number of spatial filters used as filter in the second convolutional layer. Default is 20.

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

  • activation (nn.Module, optional) – Activation function after the second convolutional layer. Default is logarithm activation.

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 not guaranteed to be correct, has not been checked by original authors, only reimplemented from the paper description and the source that have not been tested [sccnetcode].

References

[sccnet]

Wei, C. S., Koike-Akino, T., & Wang, Y. (2019, March). Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 328-331). IEEE.

[sccnetcode]

Hsieh, C. Y., Chou, J. L., Chang, Y. H., & Wei, C. S. XBrainLab: An Open-Source Software for Explainable Artificial Intelligence-Based EEG Analysis. In NeurIPS 2023 AI for Science Workshop.

Methods

forward(x: Tensor) → Tensor[source]#

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Parameters:

x – The description is missing.

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