RearrangeElectrode

class torcheeg.transforms.RearrangeElectrode(source: List[str], target: List[str], missing: str = 'mean', apply_to_baseline: bool = False)

Select parts of electrode signals based on a given electrode index list.

from torcheeg import transforms

t = transforms.RearrangeElectrode(
    source=['FP1', 'F3', 'F7'],
    target=['F3', 'F7', 'FP1', 'AF2'],
    missing='mean'
)
t(eeg=np.random.randn(3, 128))['eeg'].shape
>>> (4, 128)

Parameters:

• source (list) – The list of electrode names to be rearranged.

• target (list) – The list of electrode names to be rearranged to.

• missing (str) – The method to deal with missing electrodes. (default: 'random')

__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:

The output signals with the shape of [number of picked electrodes, number of data points].

Return type:

np.ndarray