braindecode.augmentation.ChannelsDropout¶
- class braindecode.augmentation.ChannelsDropout(probability, p_drop=0.2, random_state=None)¶
Randomly set channels to flat signal.
Part of the CMSAugment policy proposed in [1]
- Parameters
- probability: float
Float setting the probability of applying the operation.
- proba_drop: float | None, optional
Float between 0 and 1 setting the probability of dropping each channel. Defaults to 0.2.
- random_state: int | numpy.random.Generator, optional
Seed to be used to instantiate numpy random number generator instance. Used to decide whether or not to transform given the probability argument and to sample channels to erase. Defaults to None.
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.
Methods
- get_params(*batch)¶
Return transform parameters.
- Parameters
- Xtensor.Tensor
The data.
- ytensor.Tensor
The labels.
- Returns
- paramsdict
Contains
- p_dropfloat
Float between 0 and 1 setting the probability of dropping each channel.
- random_statenumpy.random.Generator
The generator to use.
- static operation(X, y, p_drop, random_state=None)¶
Randomly set channels to flat signal.
Part of the CMSAugment policy proposed in [1]
- Parameters
- Xtorch.Tensor
EEG input example or batch.
- ytorch.Tensor
EEG labels for the example or batch.
- p_dropfloat
Float between 0 and 1 setting the probability of dropping each channel.
- random_stateint | 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.