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) → tuple[ndarray, dict[str, Any]][source]#

Apply frequency-guided Basic SSA independently to every channel.

Parameters:

  • X (array-like, shape (n_channels, n_times)) – Finite channel-first data. Channels are never mixed.

  • sfreq (float) – Sampling frequency in Hz.

  • window_length (int | None, default=None) – Embedding dimension in samples. If None, it is selected automatically.

  • drop_freq_max (float, default=3.0) – Reject components whose dominant frequency is at or below this value in Hz.

  • drop_band (tuple of float | None, default=None) – Inclusive (low, high) dominant-frequency rejection band in Hz.

  • n_check (int | None, default=None) – Restrict selection to this many leading numerical-rank components. None examines every numerical-rank component.

  • max_window (int, default=100) – Maximum embedding dimension used by automatic window selection.

  • window_seconds (float | None, default=None) – Embedding duration in seconds, mutually exclusive with window_length.

Returns:

  • X_clean (ndarray, shape (n_channels, n_times)) – Cleaned data with the same shape as X.

  • info (dict) – Per-channel component-selection diagnostics and the common resolved operating point.

Raises:

  • TypeError – If a scalar parameter has an invalid type.

  • ValueError – If X, the frequency bounds, or the embedding is invalid.

See also

ssa_clean_channel

Canonical single-channel implementation.

SingularSpectrumAnalysis

MNE/scikit-learn estimator interface.

Notes

This function calls ssa_clean_channel() independently for every channel. It implements repeated univariate SSA, not multivariate SSA.