ARRCoefficient

class torcheeg.transforms.ARRCoefficient(order: int = 4, norm: str = 'biased', apply_to_baseline: bool = False)

Calculate autoregression reflection coefficients on the input data.

transform = ARRCoefficient(order=4)
transform(eeg=np.random.randn(32, 128))['eeg'].shape
>>> (32, 4)

Parameters:

• order (int) – The order of autoregressive process to be fitted. (default: 4)

• norm (str) – Use a biased or unbiased correlation. (default: biased)

• 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 or features.

• 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 autoregression reflection coefficients.

Return type:

np.ndarray [number of electrodes, order]