mne_denoise.asr.process_asr

mne_denoise.asr.process_asr(X: ndarray, sfreq: float, state: ASRState, *, window_length: float = 0.5, window_overlap: float = 0.66, max_dims: float | int = 0.66, regularization: float = 1e-08, store_reconstruction_matrices: bool = False, max_mem_mb: int | None = 512, lookahead: float | None = None, stepsize: int | None = None, method: str | None = None, callback=None, verbose: bool | str | int | None = None) → tuple[ndarray, dict[str, Any]]

Apply a calibrated ASR state to continuous data.

Parameters:

Xndarray, shape (n_channels, n_times)

Data in the fitted channel order and units.

sfreqfloat

Sampling frequency in Hz.

stateASRState

State returned by calibrate_asr.

window_lengthfloat, default=0.5

Processing window length in seconds.

window_overlapfloat, default=0.66

Overlap used for threshold windows.

max_dimsfloat or int, default=0.66

Maximum reconstructed dimensions; fractions are relative to channel count.

regularizationfloat, default=1e-8

Relative covariance eigenvalue floor.

store_reconstruction_matricesbool, default=False

Store window matrices in diagnostics.

max_mem_mbint or None, default=512

Memory cap for covariance processing.

lookaheadfloat or None, default=None

Processing lookahead in seconds.

stepsizeint or None, default=None

Samples between reconstruction updates.

method{“standard”, “riemannian”, “riemannian_windowed”} or None, default=None

Covariance backend; None uses state.method.

callbackcallable or None, default=None

Synchronous callback after each reconstruction update.

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

Logging level.

Returns:

X_cleanndarray, shape (n_channels, n_times)

Reconstructed data.

diagnosticsdict

Processing diagnostics.