Flatten¶

class torcheeg.transforms.Flatten(apply_to_baseline: bool = False)[source][source]¶

Flatten the input EEG representation.

from torcheeg import transforms

t = transforms.Flatten()
t(eeg=np.random.randn(62, 5))['eeg'].shape
>>> (310,)

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

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:

The transformed results.

Return type:

np.ndarray

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