braindecode.augmentation.ChannelsSymmetry#

class braindecode.augmentation.ChannelsSymmetry(probability, ordered_ch_names, random_state=None)[source]#

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)[source]#

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.