braindecode.models.FBCNet#

class braindecode.models.FBCNet(n_chans=None, n_outputs=None, chs_info=None, n_times=None, input_window_seconds=None, sfreq=None, n_bands=9, n_filters_spat: int = 32, temporal_layer: str = 'LogVarLayer', n_dim: int = 3, stride_factor: int = 4, activation: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.SiLU'>, linear_max_norm: float = 0.5, cnn_max_norm: float = 2.0, filter_parameters: dict[~typing.Any, ~typing.Any] | None = None)[source]#

FBCNet from Mane, R et al (2021) [fbcnet2021].

FBCNet Architecture

The FBCNet model applies spatial convolution and variance calculation along the time axis, inspired by the Filter Bank Common Spatial Pattern (FBCSP) algorithm.

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.

  • 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.

  • 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.

  • n_bands (int or None or list[tuple[int, int]]], default=9) – Number of frequency bands. Could

  • n_filters_spat (int, default=32) – Number of spatial filters for the first convolution.

  • temporal_layer (str, default='LogVarLayer') – Type of temporal aggregator layer. Options: ‘VarLayer’, ‘StdLayer’, ‘LogVarLayer’, ‘MeanLayer’, ‘MaxLayer’.

  • n_dim (int, default=3) – Number of dimensions for the temporal reductor

  • stride_factor (int, default=4) – Stride factor for reshaping.

  • activation (nn.Module, default=nn.SiLU) – Activation function class to apply in Spatial Convolution Block.

  • linear_max_norm (float, default=0.5) – Maximum norm for the final linear layer.

  • cnn_max_norm (float, default=2.0) – Maximum norm for the spatial convolution layer.

  • filter_parameters (dict, default None) – Parameters for the FilterBankLayer

Raises:

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

Notes

This implementation is not guaranteed to be correct and has not been checked by the original authors; it has only been reimplemented from the paper description and source code [fbcnetcode2021]. There is a difference in the activation function; in the paper, the ELU is used as the activation function, but in the original code, SiLU is used. We followed the code.

References

[fbcnet2021]

Mane, R., Chew, E., Chua, K., Ang, K. K., Robinson, N., Vinod, A. P., … & Guan, C. (2021). FBCNet: A multi-view convolutional neural network for brain-computer interface. preprint arXiv:2104.01233.

[fbcnetcode2021]

Link to source-code: ravikiran-mane/FBCNet

Methods

forward(x: Tensor) → Tensor[source]#

Forward pass of the FBCNet model.

Parameters:

x (torch.Tensor) – Input tensor with shape (batch_size, n_chans, n_times).

Returns:

Output tensor with shape (batch_size, n_outputs).

Return type:

torch.Tensor

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