mne.read_cov#

mne.read_cov(fname, verbose=None)[source]#

Read a noise covariance from a FIF file.

Parameters
fnamestr

The name of file containing the covariance matrix. It should end with -cov.fif or -cov.fif.gz.

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
covCovariance

The noise covariance matrix.

Examples using mne.read_cov#

Computing a covariance matrix

Computing a covariance matrix

Computing a covariance matrix
The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization
Computing various MNE solutions

Computing various MNE solutions

Computing various MNE solutions
Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix
Generate simulated evoked data

Generate simulated evoked data

Generate simulated evoked data
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals
Decoding source space data

Decoding source space data

Decoding source space data
Source localization with a custom inverse solver

Source localization with a custom inverse solver

Source localization with a custom inverse solver
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

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

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Compute Rap-Music on evoked data

Compute Rap-Music on evoked data

Compute Rap-Music on evoked data
Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior