mne_denoise.ssa.ssa_clean_channel#
- mne_denoise.ssa.ssa_clean_channel(x: ndarray, sfreq: float, window_length: int | None = None, drop_freq_max: float = 3.0, drop_band: tuple[float, float] | None = None, n_check: int | None = None, max_window: int = 100, *, window_seconds: float | None = None, return_info: bool = False) ndarray | tuple[ndarray, dict[str, Any]][source]#
Clean one channel by frequency-grouping Basic SSA components.
- Parameters:
- xarray-like, shape (n_times,)
Finite scalar time series.
- sfreqfloat
Sampling frequency in Hz.
- window_lengthint | None, default=None
Embedding dimension in samples; None selects it automatically.
- drop_freq_maxfloat, default=3.0
Upper bound, in Hz, for the dominant-frequency rejection rule when drop_band is None.
- drop_bandtuple of float | None, default=None
Inclusive dominant-frequency interval to reject, in Hz.
- n_checkint | None, default=None
Number of leading numerical-rank components to inspect; None inspects all.
- max_windowint, default=100
Maximum automatic embedding dimension.
- window_secondsfloat | None, default=None
Embedding duration in seconds, mutually exclusive with window_length.
- return_infobool, default=False
If True, also return decomposition and grouping diagnostics.
- Returns:
- x_cleanndarray, shape (n_times,)
Cleaned time series.
- infodict
Diagnostics returned only when return_info=True.
Notes
Dominant frequency is the largest real-FFT magnitude bin; DC is included. The frequency grouping and thresholds are package heuristics.