- cov : Covariance
The noise covariance matrix.
- info : dict
The measurement info (used to get channel types and bad channels).
- mag : float (default 0.1)
Regularization factor for MEG magnetometers.
- grad : float (default 0.1)
Regularization factor for MEG gradiometers. Must be the same as
mag if data have been processed with SSS.
- eeg : float (default 0.1)
Regularization factor for EEG.
- exclude : list | ‘bads’ (default ‘bads’)
List of channels to mark as bad. If ‘bads’, bads channels
are extracted from both info[‘bads’] and cov[‘bads’].
- proj : bool (default true)
Apply projections to keep rank of data.
- seeg : float (default 0.1)
Regularization factor for sEEG signals.
- ecog : float (default 0.1)
Regularization factor for ECoG signals.
- hbo : float (default 0.1)
Regularization factor for HBO signals.
- hbr : float (default 0.1)
Regularization factor for HBR signals.
- rank : None | int | dict | ‘full’
Specified rank of the noise covariance matrix. If None, the rank is
detected automatically. If int, the rank is specified for the MEG
channels. A dictionary with entries ‘eeg’, ‘meg’ or any other
data channel type such as ‘seeg’ or ‘ecog’ can be used
to specify the rank for each modality. If ‘full’ (default in 0.17),
the covariance is assumed to be full-rank when regularizing
(unless proj=True, in which case projections are accounted for).
The default in 0.18 will be None.
- scalings : dict | None
Data will be rescaled before rank estimation to improve accuracy.
See mne.compute_covariance()
.
- verbose : bool | str | int | None (default None)
If not None, override default verbose level (see mne.verbose()
).