braindecode.models.EEGITNet#

class braindecode.models.EEGITNet(n_classes, in_channels, input_window_samples, drop_prob=0.4)[source]#
EEG-ITNet: An Explainable Inception Temporal

Convolutional Network for motor imagery classification from Salami et. al 2022.

See [Salami2022] for details.

Code adapted from abbassalami/eeg-itnet

Parameters

  • n_classes (int) – number of outputs of the decoding task (for example number of classes in classification)

  • n_in_chans (int) – number of input EEG channels

  • input_window_samples (int) – Number of time samples.

  • drop_prob (float) – Dropout probability.

References

Salami2022

  1. Salami, J. Andreu-Perez and H. Gillmeister, “EEG-ITNet: An Explainable

Inception Temporal Convolutional Network for motor imagery classification,” in IEEE Access, doi: 10.1109/ACCESS.2022.3161489.

Notes

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