braindecode.models.BDTCN#

class braindecode.models.BDTCN(n_chans=None, n_outputs=None, chs_info=None, n_times=None, sfreq=None, input_window_seconds=None, n_blocks=3, n_filters=30, kernel_size=5, drop_prob=0.5, activation: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ReLU'>)[source]#

Braindecode TCN from Gemein, L et al (2020) [gemein2020].

Braindecode TCN Architecture

See [gemein2020] for details.

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.

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

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

  • n_blocks (int) – number of temporal blocks in the network

  • n_filters (int) – number of output filters of each convolution

  • kernel_size (int) – kernel size of the convolutions

  • drop_prob (float) – dropout probability

  • activation (nn.Module, default=nn.ReLU) – Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.ReLU.

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

If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.

References

[gemein2020] (1,2)

Gemein, L. A., Schirrmeister, R. T., Chrabąszcz, P., Wilson, D., Boedecker, J., Schulze-Bonhage, A., … & Ball, T. (2020). Machine-learning-based diagnostics of EEG pathology. NeuroImage, 220, 117021.

Methods

forward(x)[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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