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. (defualt: 4)

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

• apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (defualt: False)

__call__(*args, eeg: ndarray, baseline: Optional[ndarray] = 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]