mne.cov.regularize

mne.cov.regularize(cov, info, mag=0.1, grad=0.1, eeg=0.1, exclude='bads', proj=True, *, seeg=0.1, ecog=0.1, hbo=0.1, hbr=0.1, fnirs_cw_amplitude=0.1, fnirs_fd_ac_amplitude=0.1, fnirs_fd_phase=0.1, fnirs_od=0.1, fnirs_td_gated_amplitude=0.1, fnirs_td_moments_intensity=0.1, fnirs_td_moments_mean=0.1, fnirs_td_moments_variance=0.1, csd=0.1, dbs=0.1, rank=None, scalings=None, verbose=None)

Regularize noise covariance matrix.

This method works by adding a constant to the diagonal for each channel type separately. Special care is taken to keep the rank of the data constant.

Note

This function is kept for reasons of backward-compatibility. Please consider explicitly using the method parameter in mne.compute_covariance() to directly combine estimation with regularization in a data-driven fashion. See the FAQ for more information.

Parameters:

covCovariance

The noise covariance matrix.

infomne.Info

The mne.Info object with information about the sensors and methods of measurement. (Used to get channel types and bad channels).

magfloat

Regularization factor for MEG magnetometers.

gradfloat

Regularization factor for MEG gradiometers. Must be the same as mag if data have been processed with SSS.

eegfloat

Regularization factor for EEG.

excludelist | ‘bads’

List of channels to mark as bad. If ‘bads’, bads channels are extracted from both info[‘bads’] and cov[‘bads’].

projbool

Apply projections to keep rank of data.

seegfloat

Regularization factor for sEEG signals.

ecogfloat

Regularization factor for ECoG signals.

hbofloat

Regularization factor for HBO signals.

hbrfloat

Regularization factor for HBR signals.

fnirs_cw_amplitudefloat

Regularization factor for fNIRS CW raw signals.

fnirs_fd_ac_amplitudefloat

Regularization factor for fNIRS FD AC raw signals.

fnirs_fd_phasefloat

Regularization factor for fNIRS raw phase signals.

fnirs_odfloat

Regularization factor for fNIRS optical density signals.

fnirs_td_gated_amplitudefloat

Regularization factor for fNIRS time domain gated amplitude signals.

fnirs_td_moments_intensityfloat

Regularization factor for fNIRS time domain moments amplitude signals.

fnirs_td_moments_meanfloat

Regularization factor for fNIRS time domain moments mean signals.

fnirs_td_moments_variancefloat

Regularization factor for fNIRS time domain moments variance signals.

csdfloat

Regularization factor for EEG-CSD signals.

dbsfloat

Regularization factor for DBS signals.

rankNone | ‘info’ | ‘full’ | dict

This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).

None

The rank will be estimated from the data after proper scaling of different channel types.

'info'

The rank is inferred from info. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.

'full'

The rank is assumed to be full, i.e. equal to the number of good channels. If a Covariance is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.

dict

Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.

This parameter must be a dictionary whose keys correspond to channel types in the data (e.g. 'meg', 'mag', 'grad', 'eeg'), and whose values are integers representing the respective ranks. For example, {'mag': 90, 'eeg': 45} will assume a rank of 90 and 45 for magnetometer data and EEG data, respectively.

The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.

The default is None.

New in v0.17.

New in v0.18: Support for ‘info’ mode.

scalingsdict | None

Data will be rescaled before rank estimation to improve accuracy. See mne.compute_covariance().

New in v0.17.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

reg_covCovariance

The regularized covariance matrix.

See also

mne.compute_covariance

Examples using mne.cov.regularize

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

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