ToTensor

class torcheeg.transforms.ToTensor(apply_to_baseline: bool = False)

Convert a numpy.ndarray to tensor. Different from torchvision, tensors are returned without scaling.

from torcheeg import transforms

t = transforms.ToTensor()
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 128)

Parameters:

apply_to_baseline (bool) – Whether to apply the transform to the baseline signal. (default: False)

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

Parameters:

• eeg (np.ndarray) – The input EEG signals.

• baseline (np.ndarray, 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:

If baseline is passed and apply_to_baseline is set to True, then {‘eeg’: …, ‘baseline’: …}, else {‘eeg’: …}. The output is represented by torch.Tensor.

Return type:

dict