ARRCoefficient¶

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

Calculate autoregression reflection coefficients on the input data.

from torcheeg import transforms

t = transforms.ARRCoefficient(order=4)
t(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][source][source]¶

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]

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