RandomFlip

class torcheeg.transforms.RandomFlip(dim=-1, p: float = 0.5, apply_to_baseline: bool = False)

Applies a random transformation with a given probability to reverse the direction of the input signal in the specified dimension, commonly used for left-right and bottom-up reversal of EEG caps and reversal of timing.

transform = RandomFlip(dim=-1)
transform(eeg=torch.randn(32, 128))['eeg'].shape
>>> (32, 128)

transform = RandomFlip(dim=1)
transform(eeg=torch.randn(128, 9, 9))['eeg'].shape
>>> (128, 9, 9)

Parameters:

• dim (int) – Dimension to be flipped 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 flipping.

Return type:

torch.Tensor