braindecode.augmentation.functional.channels_dropout#

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

Randomly set channels to flat signal.

Part of the CMSAugment policy proposed in [1]

Parameters

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

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

  • p_drop (float) – Float between 0 and 1 setting the probability of dropping each channel.

  • random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.

Returns

  • torch.Tensor – Transformed inputs.

  • torch.Tensor – Transformed labels.

References

1

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