RandomChannelShuffle

class torcheeg.transforms.RandomChannelShuffle(p: float = 0.5, apply_to_baseline: bool = False)

Apply a shuffle with a specified probability, after which the order of the channels is randomly shuffled.

transform = RandomChannelShuffle()
transform(eeg=torch.randn(32, 128))['eeg'].shape
>>> (32, 128)

Parameters:

• p (float) – Probability of applying random mask on EEG signal samples. Should be between 0.0 and 1.0, where 0.0 means no mask is applied to every sample and 1.0 means that masks are applied to every sample. (default: 0.5)

• apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (default: False)

__call__(*args, eeg: Tensor, baseline: Tensor | None = None, **kwargs) → Dict[str, Tensor]

Parameters:

• eeg (torch.Tensor) – The input EEG signal.

• baseline (torch.Tensor, optional) – The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.

Returns:

The output EEG signal after applying a random channel shuffle.

Return type:

torch.Tensor