braindecode.augmentation.SmoothTimeMask¶
- class braindecode.augmentation.SmoothTimeMask(probability, mask_len_samples=100, random_state=None)¶
Smoothly replace a randomly chosen contiguous part of all channels by zeros.
- Parameters
- probabilityfloat
Float setting the probability of applying the operation.
- mask_len_samplesint | 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_params(*batch)¶
Return transform parameters.
- Parameters
- Xtensor.Tensor
The data.
- ytensor.Tensor
The labels.
- Returns
- paramsdict
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.
- 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
- 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.