BandKurtosis

class torcheeg.transforms.BandKurtosis(sampling_rate: int = 128, order: int = 5, band_dict: Dict[str, Tuple[int, int]] = {'alpha': [8, 14], 'beta': [14, 31], 'gamma': [31, 49], 'theta': [4, 8]}, apply_to_baseline: bool = False)

A transform method for calculating the kurtosis of EEG signals in several sub-bands with EEG signals as input.

from torcheeg import transforms

t = transforms.BandKurtosis()
t(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 4)

Parameters:

• sampling_rate (int) – The original sampling rate of EEG signals in Hz. (default: 128)

• order (int) – The order of the filter. (default: 5)

• band_dict – (dict): Band name and the critical sampling rate or frequencies. By default, the kurtosis of the four sub-bands, theta, alpha, beta and gamma, is calculated. (default: {...})

__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 kurtosis of several sub-bands for all electrodes.

Return type:

np.ndarray[number of electrodes, number of sub-bands]