mne.make_ad_hoc_cov#

mne.make_ad_hoc_cov(info, std=None, verbose=None)[source]#

Create an ad hoc noise covariance.

Parameters:
infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

stddict of float | None

Standard_deviation of the diagonal elements. If dict, keys should be 'grad' for gradiometers, 'mag' for magnetometers and 'eeg' for EEG channels. If None, default values will be used (see Notes).

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:
covinstance of Covariance

The ad hoc diagonal noise covariance for the M/EEG data channels.

Notes

The default noise values are 5 fT/cm, 20 fT, and 0.2 µV for gradiometers, magnetometers, and EEG channels respectively.

New in version 0.9.0.

Examples using mne.make_ad_hoc_cov#

DICS for power mapping

DICS for power mapping

DICS for power mapping
Generate simulated raw data

Generate simulated raw data

Generate simulated raw data
Generate simulated source data

Generate simulated source data

Generate simulated source data