mne_denoise.dss.denoisers.SmoothingBias

class mne_denoise.dss.denoisers.SmoothingBias(window: int = 10, iterations: int = 1)

Unified temporal smoothing bias (Moving Average).

Uses a boxcar moving average filter to smooth the data. When used to split the signal into a smooth branch and a residual (data - smooth), fitting DSS on the residual and adding the smooth branch back follows ZapLine’s period-matched decomposition (de Cheveigné, 2020): with window = round(sfreq / f_line) the smoother has zeros at f_line and its harmonics, so the residual concentrates the narrowband artifact.

Parameters:

• window (int) – Smoothing window size in samples. Note: If you want to cancel a specific frequency (e.g. 50Hz line noise), set window = int(sfreq / 50).

• iterations (int) – Number of smoothing passes. Repeated smoothing approximates a Gaussian filter and provides sharper frequency cutoff. Default 1.

Examples

>>> bias = SmoothingBias(window=20)  # Simple smoothing
>>> biased = bias.apply(data)

>>> # To remove 50Hz line noise (Period smoothing)
>>> bias = SmoothingBias(window=int(1000 / 50), iterations=1)

__init__(window: int = 10, iterations: int = 1) → None

Methods

__init__([window, iterations])
apply(data)	Apply smoothing bias.