braindecode.models.TCN

class braindecode.models.TCN(n_in_chans, n_outputs, n_blocks, n_filters, kernel_size, drop_prob, add_log_softmax)

Temporal Convolutional Network (TCN) from Bai et al 2018.

See [Bai2018] for details.

Code adapted from https://github.com/locuslab/TCN/blob/master/TCN/tcn.py

Parameters

n_in_chans: int

number of input EEG channels

n_outputs: int

number of outputs of the decoding task (for example number of classes in classification)

n_filters: int

number of output filters of each convolution

n_blocks: int

number of temporal blocks in the network

kernel_size: int

kernel size of the convolutions

drop_prob: float

dropout probability

add_log_softmax: bool

whether to add a log softmax layer

References

Bai2018

Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

Methods

forward(x)

Forward pass.

Parameters

x: torch.Tensor

Batch of EEG windows of shape (batch_size, n_channels, n_times).