braindecode.augmentation.functional.smooth_time_mask¶
- braindecode.augmentation.functional.smooth_time_mask(X, y, mask_start_per_sample, mask_len_samples)¶
Smoothly replace a contiguous part of all channels by zeros.
Originally proposed in [1] and [2]
- Parameters
- Xtorch.Tensor
EEG input example or batch.
- ytorch.Tensor
EEG labels for the example or batch.
- mask_start_per_sampletorch.tensor
Tensor of integers containing the position (in last dimension) where to start masking the signal. Should have the same size as the first dimension of X (i.e. one start position per example in the batch).
- mask_len_samplesint
Number of consecutive samples to zero out.
- Returns
- torch.Tensor
Transformed inputs.
- torch.Tensor
Transformed labels.
References
- 1
Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020). Subject-aware contrastive learning for biosignals. arXiv preprint arXiv:2007.04871.
- 2
Mohsenvand, M. N., Izadi, M. R., & Maes, P. (2020). Contrastive Representation Learning for Electroencephalogram Classification. In Machine Learning for Health (pp. 238-253). PMLR.