mne.preprocessing.regress_artifact¶

mne.preprocessing.regress_artifact(inst, picks=None, picks_artifact='eog', betas=None, copy=True, verbose=None)[source]¶

Regress artifacts using reference channels.

Parameters

instinstance of Epochs | Raw: The instance to process.
picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels.
picks_artifactarray_like | str: Channel picks to use as predictor/explanatory variables capturing the artifact of interest (default is “eog”).
betasndarray, shape (n_picks, n_picks_ref) | None: The regression coefficients to use. If None (default), they will be estimated from the data.
copybool: If True (default), copy the instance before modifying it.
verbosebool, str, int, or None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

instinstance of Epochs | Raw: The processed data.
betasndarray, shape (n_picks, n_picks_ref): The betas used during regression.

Notes

To implement the method outlined in 1, remove the evoked response from epochs before estimating the regression coefficients, then apply those regression coefficients to the original data in two calls like (here for a single-condition epochs only):

>>> epochs_no_ave = epochs.copy().subtract_evoked()  
>>> _, betas = mne.preprocessing.regress(epochs_no_ave)  
>>> epochs_clean, _ = mne.preprocessing.regress(epochs, betas=betas)  

References

1: Gabriele Gratton, Michael G. H Coles, and Emanuel Donchin. A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4):468–484, April 1983. doi:10.1016/0013-4694(83)90135-9.