braindecode.augmentation.functional.channels_shuffle(X, y, p_shuffle, random_state=None)[source]#

Randomly shuffle channels in EEG data matrix.

Part of the CMSAugment policy proposed in [1]

  • X (torch.Tensor) – EEG input example or batch.

  • y (torch.Tensor) – EEG labels for the example or batch.

  • p_shuffle (float | None) – Float between 0 and 1 setting the probability of including the channel in the set of permutted channels.

  • random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Used to sample which channels to shuffle and to carry the shuffle. Defaults to None.


  • torch.Tensor – Transformed inputs.

  • torch.Tensor – Transformed labels.



Saeed, A., Grangier, D., Pietquin, O., & Zeghidour, N. (2020). Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering. arXiv preprint arXiv:2010.13694.