DWTDecomposition

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

Splitting the EEG signal from each electrode into two functions using wavelet decomposition.

transform = DWTDecomposition()
transform(eeg=np.random.randn(32, 1000))['eeg'].shape
>>> (32, 500)

Parameters:

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: ndarray, baseline: ndarray | None = None, **kwargs) → Dict[str, ndarray]

Parameters:

• eeg (np.ndarray) – The input EEG signals in shape of [number of electrodes, number of data points].

• 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:

EEG signal after wavelet decomposition, where 2 corresponds to the two functions of the wavelet decomposition, and number of data points / 2 represents the length of each component

Return type:

np.ndarray[number of electrodes, 2, number of data points / 2]