braindecode.augmentation.functional.cutmix#

braindecode.augmentation.functional.cutmix(X, y, idx_perm, cut_t_start, cut_t_len)[source]#

Paste a time region of shuffled examples into the original examples.

See [1] for details.

Parameters:

  • X (Tensor) – EEG data in form batch_size, n_channels, n_times

  • y (Tensor) – Target of length batch_size

  • idx_perm (Tensor) – Permuted indices of example that are mixed into original examples.

  • cut_t_start (Tensor) – Start index of the pasted time region for each example.

  • cut_t_len (Tensor) – Length of the pasted time region for each example.

Returns:

X, y. Where X is augmented and y is a tuple of length 3 containing the labels of the two mixed examples and the fraction of each example kept from the original, 1 - cut_t_len / n_times.

Return type:

tuple[Tensor, Tensor]

References

[1]

Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo (2019). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) Online: https://arxiv.org/abs/1905.04899

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