PickElectrode

class torcheeg.transforms.PickElectrode(pick_list: List[int], apply_to_baseline: bool = False)

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

transform = PickElectrode(PickElectrode.to_index_list(
    ['FP1', 'AF3', 'F3', 'F7',
     'FC5', 'FC1', 'C3', 'T7',
     'CP5', 'CP1', 'P3', 'P7',
     'PO3','O1', 'FP2', 'AF4',
     'F4', 'F8', 'FC6', 'FC2',
     'C4', 'T8', 'CP6', 'CP2',
     'P4', 'P8', 'PO4', 'O2'], DEAP_CHANNEL_LIST))
transform(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (28, 128)

Parameters

• pick_list (np.ndarray) – Selected electrode list. Should consist of integers representing the corresponding electrode indices. to_index_list can be used to obtain an index list when we only know the names of the electrode and not their indices.

• apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (defualt: False)

__call__(*args, eeg: ndarray, baseline: Optional[ndarray] = 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