FBCNet

class selfeeg.models.zoo.FBCNet(nb_classes: int, Chans: int, Samples: int, Fs: int, FilterBands: int = 9, FilterRange: float = 4, FilterType: str = 'Cheby2', FilterStopRippple: int = 30, FilterPassRipple: int = 3, FilterRangeTol: int = 2, FilterSkipFirst=True, D: int = 32, TemporalType: str = 'logvar', TemporalStride: int = 4, batch_momentum: float = 0.1, depthwise_max_norm: float = None, linear_max_norm: float = None, classifier: Module = None, return_logits: bool = True, seed: int = None)[source]

Pytorch implementation of the FBCNet model.

FBCNet paper can be found here [fbcnet] . The official implementation can be found here [gitfbc] .

The expected input is a 3D tensor with size (Batch x Channels x Samples).

Filter operation is applied through the torchaudio filtfilt function. Do not use too strict filter settings as this might generate nan or too high values.

Parameters:

  • nb_classes (int) – The number of classes. If less than 2, a binary classification problem is considered (output dimensions will be [batch, 1] in this case).

  • Chans (int) – The number of EEG channels.

  • Samples (int) – The number of EEG samples.

  • Fs (int or float) – The EEG sampling rate.

  • FilterBands (int, optional) –

    The number of filters to apply to the original signal. It is used by the FilterBank layer.

    Default = 9

  • FilterRange (int or float, optional) –

    The passband of each filter, given in Hz.It is used by the FilterBank layer.

    Default = 4

  • FilterType (str, optional) –

    The type of filter to use. Allowed arguments are the same as described in the get_filter_coeff() function of the selfeeg.augmentation.functional submodule (‘butter’, ‘ellip’, ‘cheby1’, ‘cheby2’). It is used by the FilterBank layer.

    Default = ‘cheby1’

  • FilterStopRipple (int or float, optional) –

    Ripple at stopband in decibel. It is used by the FilterBank layer.

    Default = 30

  • FilterPassRipple (int or float, optional) –

    Ripple at passband in decibel. It is used by the FilterBank layer.

    Default = 30

  • FilterRangeTol (int or float, optional) –

    The filter transition bandwidth in Hz. It is used by the FilterBank layer.

    Default = 2

  • FilterSkipFirst (bool, optional) –

    If True, skips the first filter with passband equal to [0, Range] Hz. The number of filters specified in Bands will still be preserved. It is used by the FilterBank layer.

    Default = True

  • D (int, optional) –

    The depth of the depthwise convolutional layer.

    Default = 2

  • TemporalType (str, optional) –

    The type of temporal feature extraction layer to use. Accepted values are ‘max’, ‘mean’, ‘std’, ‘var’, or ‘logvar’.

    Default = ‘logvar’

  • TemporalStride (int, optional) –

    The signal length output dimension of the temporal feature extraction layer. Kernel length and layer stride will be calculated based on the given input. Be sure that Sample is a multiple of this attribute.

    Default = 4

  • batch_momentum (float, optional) –

    The batch normalization momentum.

    Default = 0.1

  • depthwise_max_norm (float, optional) –

    The maximum norm each filter can have in the depthwise block. If None no constraint will be included.

    Default = None

  • linear_max_norm (float, optional) –

    The maximum norm each filter can have in the final dense layer. If None no constraint will be included.

    Default = None

  • classifier (nn.Module, optional) –

    A custom block to apply after the encoder instead of the classical linear layer. Must be an istance of an nn.Module. If none a standard linear layer is applied.

    Default = None

  • return_logits (bool, optional) –

    Whether to return the output as logit or probability. It is suggested to not use False as the pytorch crossentropy applies the softmax internally.

    Default = True

  • seed (int, optional) –

    A custom seed for model initialization. It must be a nonnegative number. If None is passed, no custom seed will be set

    Default = None

References

[fbcnet]

Mane et al. FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface. https://arxiv.org/abs/2104.01233

Example

>>> import selfeeg.models
>>> import torch
>>> x = torch.randn(4,8,512)
>>> mdl = models.FBCNet(2, 8, 512, 128)
>>> out = mdl(x)
>>> print(out.shape) # shoud return torch.Size([4, 2])
>>> print(torch.isnan(out).sum()) # shoud return 0