PearsonCorrelation¶

class torcheeg.transforms.PearsonCorrelation(absolute: bool = False, apply_to_baseline: bool = False)[source][source]¶

A transform method to calculate the correlation coefficients between the EEG signals of different electrodes.

from torcheeg import transforms

t = transforms.PearsonCorrelation()
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (1, 32, 32)

Parameters:

absolute (bool) – Whether to take the absolute value of the correlation coefficient. (default: 128)
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][source][source]¶

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 correlation coefficients between EEG signals of different electrodes.

Return type:

np.ndarray[number of electrodes, number of electrodes]

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