mne_denoise.dss.denoisers.TimeShiftBias#

class mne_denoise.dss.denoisers.TimeShiftBias(shifts: int | ndarray = 10, method: str = 'autocorrelation')[source]#

Time-shift bias for extracting autocorrelated signals.

Creates a bias by averaging time-shifted versions of the data, emphasizing signals that are predictable across time lags.

Parameters:

  • shifts (int or array-like) – If int, use lags from 1 to shifts. If array, use specified lag values in samples. Default 10.

  • method (str) – Method for constructing bias: - ‘autocorrelation’: Average of shifted versions (default) - ‘prediction’: Weighted average (closer lags weighted more)

Examples

>>> bias = TimeShiftBias(shifts=[1, 2, 5, 10], method="prediction")
>>> biased_data = bias.apply(data)

See also

SmoothingBias

Bias for low-frequency signals.

__init__(shifts: int | ndarray = 10, method: str = 'autocorrelation') → None[source]#

Methods

__init__([shifts, method])

apply(data)

Apply time-shift bias.