ToGrid

class torcheeg.transforms.ToGrid(channel_location_dict: Dict[str, Tuple[int, int]], apply_to_baseline: bool = False)

A transform method to project the EEG signals of different channels onto the grid according to the electrode positions to form a 3D EEG signal representation with the size of [number of data points, width of grid, height of grid]. For the electrode position information, please refer to constants grouped by dataset:

• datasets.constants.emotion_recognition.deap.DEAP_CHANNEL_LOCATION_DICT

• datasets.constants.emotion_recognition.dreamer.DREAMER_CHANNEL_LOCATION_DICT

• datasets.constants.emotion_recognition.seed.SEED_CHANNEL_LOCATION_DICT

• …

from torcheeg import transforms
from torcheeg.datasets.constants import DEAP_CHANNEL_LOCATION_DICT

t = transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (128, 9, 9)

Parameters:

• channel_location_dict (dict) – Electrode location information. Represented in dictionary form, where key corresponds to the electrode name and value corresponds to the row index and column index of the electrode on the grid.

• 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 projected results with the shape of [number of data points, width of grid, height of grid].

Return type:

np.ndarray

reverse(eeg: ndarray, **kwargs) → ndarray

The inverse operation of the converter is used to take out the electrodes on the grid and arrange them in the original order. :param eeg: The input EEG signals in shape of [number of data points, width of grid, height of grid]. :type eeg: np.ndarray

Returns:

The revered results with the shape of [number of electrodes, number of data points].

Return type:

np.ndarray