braindecode.models.EEGNetv1#

class braindecode.models.EEGNetv1(n_chans=None, n_outputs=None, n_times=None, final_conv_length='auto', pool_mode='max', second_kernel_size=(2, 32), third_kernel_size=(8, 4), drop_prob=0.25, activation: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ELU'>, chs_info=None, input_window_seconds=None, sfreq=None)[source]#

EEGNet model from Lawhern et al. 2016 from [EEGNet].

See details in [EEGNet].

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.

  • n_times (int) – Number of time samples of the input window.

  • final_conv_length – The description is missing.

  • pool_mode – The description is missing.

  • second_kernel_size – The description is missing.

  • third_kernel_size – The description is missing.

  • drop_prob – The description is missing.

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

  • 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.

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

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

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

This implementation is not guaranteed to be correct, has not been checked by original authors, only reimplemented from the paper description.

References

[EEGNet] (1,2)

Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2016). EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces. arXiv preprint arXiv:1611.08024.

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