BandMeanAbsoluteDeviation¶
- class torcheeg.transforms.BandMeanAbsoluteDeviation(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)[source][source]¶
A transform method for calculating the mean absolute deviation of EEG signals in several sub-bands with EEG signals as input.
transform = BandMeanAbsoluteDeviation() transform(eeg=np.random.randn(32, 128))['eeg'].shape >>> (32, 4)
- Parameters
sampling_rate (int) – The original sampling rate of EEG signals in Hz. (defualt:
128)order (int) – The order of the filter. (defualt:
5)band_dict – (dict): Band name and the critical sampling rate or frequencies. By default, the mean absolute deviation of the four sub-bands, theta, alpha, beta and gamma, is calculated. (defualt:
{...})
- __call__(*args, eeg: ndarray, baseline: Optional[ndarray] = 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 mean absolute deviation of several sub-bands for all electrodes.
- Return type
np.ndarray[number of electrodes, number of sub-bands]
