braindecode.augmentation.SmoothTimeMask#

class braindecode.augmentation.SmoothTimeMask(probability, mask_len_samples=100, random_state=None)[source]#

Smoothly replace a randomly chosen contiguous part of all channels by zeros.

Suggested e.g. in [1] and [2]

Parameters

probability (float) – Float setting the probability of applying the operation.
mask_len_samples (int | torch.Tensor, optional) – Number of consecutive samples to zero out. Will be ignored if magnitude is not set to None. Defaults to 100.
random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.

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.

Methods

get_augmentation_params(*batch)[source]#

Return transform parameters.

Parameters

Returns

params – Contains two elements:

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.

Return type

dict

static operation(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

X (torch.Tensor) – EEG input example or batch.
y (torch.Tensor) – EEG labels for the example or batch.
mask_start_per_sample (torch.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_samples (int) – Number of consecutive samples to zero out.

Returns

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.

Examples using `braindecode.augmentation.SmoothTimeMask`#