RandomShift

class torcheeg.transforms.RandomShift(p: float = 0.5, shift_min: int = 8, shift_max: int = 12, dim: int = -1, apply_to_baseline: bool = False)

Apply a shift with a specified probability, after which the specified dimension is shifted backward, and the part shifted out of the Tensor is added to the front of that dimension.

transform = RandomShift(dim=-1, shift_min=8, shift_max=24)
transform(eeg=torch.randn(32, 128))['eeg'].shape
>>> (32, 128)

Parameters:

• shift_min (float or int) – The minimum shift in the random transformation. (default: -2.0)

• shift_max (float or int) – The maximum shift in random transformation. (default: 2.0)

• dim (int) – Dimension to be shifted in the input tensor. (default: -1)

• 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 shift.

Return type:

torch.Tensor