mne.preprocessing.regress_artifact#
- mne.preprocessing.regress_artifact(inst, picks=None, *, exclude='bads', picks_artifact='eog', betas=None, proj=True, copy=True, verbose=None)[source]#
Remove artifacts using regression based on reference channels.
- Parameters:
- instinstance of
Epochs
|Raw
The instance to process.
- picks
str
| array_like |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. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- exclude
list
| ‘bads’ List of channels to exclude from the regression, only used when picking based on types (e.g., exclude=”bads” when picks=”meg”). Specify
'bads'
(the default) to exclude all channels marked as bad.New in version 1.2.
- picks_artifactarray_like |
str
Channel picks to use as predictor/explanatory variables capturing the artifact of interest (default is “eog”).
- betas
ndarray
, shape (n_picks, n_picks_ref) |None
The regression coefficients to use. If None (default), they will be estimated from the data.
- proj
bool
Whether to automatically apply SSP projection vectors before performing the regression. Default is
True
.- copy
bool
If True (default), copy the instance before modifying it.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- instinstance of
- Returns:
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