mne_denoise.ssa.local_ssa_clean_channel#
- mne_denoise.ssa.local_ssa_clean_channel(x: ndarray, window_length: int | None = None, *, window_seconds: float | None = None, sfreq: float | None = None, n_clusters: int | str = 'auto', max_clusters: int = 10, max_window: int = 100, random_state: int | None = 0, return_info: bool = False) ndarray | tuple[ndarray, dict[str, Any]][source]#
Clean one channel with clustered local SSA reconstruction.
- Parameters:
- xarray-like, shape (n_times,)
Finite scalar time series.
- window_lengthint | None, default=None
Delay-vector dimension in samples; None selects it automatically.
- window_secondsfloat | None, default=None
Delay-vector duration in seconds, requiring sfreq and mutually exclusive with window_length.
- sfreqfloat | None, default=None
Sampling frequency in Hz.
- n_clustersint or “auto”, default=”auto”
Number of delay-vector clusters, or automatic reliable selection.
- max_clustersint, default=10
Upper bound for automatic cluster selection.
- max_windowint, default=100
Maximum automatic delay-vector dimension.
- random_stateint | None, default=0
Seed passed to k-means.
- return_infobool, default=False
If True, also return clustering and reconstruction diagnostics.
- Returns:
- x_cleanndarray, shape (n_times,)
Residual after subtracting the local-subspace reconstruction.
- infodict
Diagnostics returned only when return_info=True.
Notes
Delay vectors are clustered, projected onto the cluster-specific MDL-selected subspaces, and reconstructed by anti-diagonal averaging. Genuine structure matching the selected subspaces can also be removed. [1].
References