mne_denoise.ssa.compute_local_ssa

mne_denoise.ssa.compute_local_ssa(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, callback=None, verbose: bool | str | int | None = None) → tuple[ndarray, dict[str, Any]]

Apply local SSA independently to each channel.

Parameters:

Xarray-like, shape (n_channels, n_times)

Finite channel-first data. Channels are not mixed.

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.

callbackcallable | None, default=None

Synchronous callback after each channel; return values are ignored and callback exceptions propagate.

verbosebool, str, int, or None

Logging level.

Returns:

X_cleanndarray, shape (n_channels, n_times)

Independently cleaned channels.

infodict

Per-channel cluster, subspace, and reconstruction diagnostics.

Notes

This is repeated univariate local SSA, not multivariate SSA.