mne_denoise.asr.compute_clean_window_mask#

mne_denoise.asr.compute_clean_window_mask(X: ndarray, sfreq: float, *, max_bad_channels: float | int = 0.2, zthresholds: tuple[float, float] = (-3.5, 5.0), window_length: float = 1.0, window_overlap: float = 0.66, max_dropout_fraction: float = 0.1, min_clean_fraction: float = 0.25, fit_quantiles: tuple[float, float] = (0.022, 0.6), beta_grid: ndarray | None = None) → tuple[ndarray, dict[str, Any]][source]#

Compute a retained-sample mask from ASR window statistics.

Parameters:

Xndarray, shape (n_channels, n_times): Continuous data.
sfreqfloat: Sampling frequency in Hz.
max_bad_channelsfloat or int, default=0.2: Maximum bad-channel fraction or count per window.
zthresholdstuple of float, default=(-3.5, 5.0): Lower and upper channel-RMS z-score limits.
window_lengthfloat, default=1.0: Window length in seconds.
window_overlapfloat, default=0.66: Window overlap fraction.
max_dropout_fractionfloat, default=0.1: Low-tail fraction ignored during RMS fitting.
min_clean_fractionfloat, default=0.25: Minimum clean fraction used for RMS fitting.
fit_quantilestuple of float, default=(0.022, 0.6): Quantile interval for the RMS fit.
beta_gridndarray or None, default=None: Optional generalized-Gaussian shape grid.

Returns: