mne_denoise.icanclean.compute_icanclean

mne_denoise.icanclean.compute_icanclean(X_primary: ndarray, X_ref: ndarray, sfreq: float, mode: str = 'sliding', clean_with: str = 'X', segment_len: float = 2.0, overlap: float = 0.0, threshold: float | str = 0.7, max_reject_fraction: float = 0.5, reref_primary: bool | str = False, reref_ref: bool | str = False, stats_segment_len: float | None = None, null_random_state: int | None = None, verbose: bool | str | int | None = None, callback=None) → tuple[ndarray, dict[str, Any]]

Compute one iCanClean pass on continuous NumPy arrays.

Parameters:

X_primaryndarray, shape (n_primary, n_times)

Primary channels to clean.

X_refndarray, shape (n_reference, n_times)

Reference channels.

sfreqfloat

Sampling frequency in Hz.

mode{“sliding”, “global”, “calibrated”}, default=”sliding”

CCA fitting and cleaning mode.

clean_with{“X”, “Y”, “both”}, default=”X”

Canonical basis used for artifact regression.

segment_lenfloat, default=2.0

Cleaning-window length in seconds for windowed modes.

overlapfloat, default=0.0

Fractional window overlap.

thresholdfloat or {“auto”, “null”}, default=0.7

Squared-correlation rejection threshold.

max_reject_fractionfloat, default=0.5

Maximum fraction of components removed per window.

reref_primarybool or str, default=False

Average-reference option for the primary CCA block.

reref_refbool or str, default=False

Average-reference option for the reference CCA block.

stats_segment_lenfloat or None, default=None

Optional broader statistics window for sliding mode.

null_random_stateint or None, default=None

Seed for threshold=”null”.

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

Logging level.

callbackcallable or None, default=None

Synchronous callback for completed window operations.

Returns:

X_primary_cleanndarray, shape (n_primary, n_times)

Cleaned primary data.

qcdict

Quality-control arrays and resolved settings.

Notes

The function is array-only and transductive: the operator is estimated from the same recording it cleans. Use ICanClean for MNE containers and estimator lifecycle semantics. A high canonical correlation indicates shared variance, not artifact identity [1][2][3].

References

[1]

Ryan J. Downey and Daniel P. Ferris. The icanclean algorithm: how to remove artifacts using reference noise recordings. 2022. arXiv:2201.11798, doi:10.48550/arXiv.2201.11798.

[2]

Ryan J. Downey and Daniel P. Ferris. Icanclean removes motion, muscle, eye, and line-noise artifacts from phantom eeg. Sensors, 23(19):8214, 2023. doi:10.3390/s23198214.

[3]

Colton B. Gonsisko, Daniel P. Ferris, and Ryan J. Downey. Icanclean improves independent component analysis of mobile brain imaging with eeg. Sensors, 23(2):928, 2023. doi:10.3390/s23020928.