braindecode.models.TCN#

class braindecode.models.TCN(n_in_chans, n_outputs, n_blocks, n_filters, kernel_size, drop_prob, add_log_softmax)[source]#

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

See [Bai2018] for details.

Code adapted from locuslab/TCN

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)[source]#

Forward pass.

Parameters

x (torch.Tensor) – Batch of EEG windows of shape (batch_size, n_channels, n_times).