mne_denoise.ssa.compute_basic_ssa

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

Apply frequency-guided Basic SSA independently to each channel.

Parameters:

Xarray-like, shape (n_channels, n_times)

Finite channel-first data. Channels are not mixed.

sfreqfloat

Sampling frequency in Hz.

window_lengthint | None, default=None

Embedding dimension in samples; None selects it automatically.

drop_freq_maxfloat, default=3.0

Dominant-frequency upper bound in Hz.

drop_bandtuple of float | None, default=None

Inclusive dominant-frequency rejection interval in Hz.

n_checkint | None, default=None

Number of leading numerical-rank components to inspect.

max_windowint, default=100

Maximum automatic embedding dimension.

window_secondsfloat | None, default=None

Embedding duration in seconds, mutually exclusive with window_length.

callbackcallable | None, default=None

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

verbosebool, str, int, or None, default=None

Logging level.

Returns:

X_cleanndarray, shape (n_channels, n_times)

Independently cleaned channels.

infodict

Per-channel selection diagnostics and the resolved operating point.

Notes

This is repeated univariate SSA, not a multivariate decomposition.