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.

from torcheeg import transforms
from torcheeg.datasets.constants import DEAP_CHANNEL_LIST

t = transforms.PickElectrode(transforms.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))
t(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. (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:

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

Return type:

np.ndarray