braindecode.augmentation.ChannelsSymmetry

class braindecode.augmentation.ChannelsSymmetry(probability, ordered_ch_names, random_state=None)

Permute EEG channels inverting left and right-side sensors.

Suggested e.g. in [1]

Parameters:

• probability (float) – Float setting the probability of applying the operation.

• ordered_ch_names (list) – Ordered list of strings containing the names (in 10-20 nomenclature) of the EEG channels that will be transformed. The first name should correspond the data in the first row of X, the second name in the second row and so on.

• 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. Defaults to None.

References

[1]

Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., & Sun, J. (2018). HAMLET: interpretable human and machine co-learning technique. arXiv preprint arXiv:1803.09702.

Methods

get_augmentation_params(*batch)

Return transform parameters.

Parameters:

• X (tensor.Tensor) – The data.

• y (tensor.Tensor) – The labels.

Returns:

params – Contains

• permutationfloat

  List of integers defining the new channels order.

Return type:

dict

static operation(X, y, permutation)

Permute EEG channels according to fixed permutation matrix.

Suggested e.g. in [1]

Parameters:

• X (torch.Tensor) – EEG input example or batch.

• y (torch.Tensor) – EEG labels for the example or batch.

• permutation (list) – List of integers defining the new channels order.

Returns:

• torch.Tensor – Transformed inputs.

• torch.Tensor – Transformed labels.

References

[1]

Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., & Sun, J. (2018). HAMLET: interpretable human and machine co-learning technique. arXiv preprint arXiv:1803.09702.