Computing the Noise Covariance Matrix

The noise covariance matrix is needed for the computation of the inverse operator.

Ingredients for this script are

* raw MEG data files (e.g. those used for averaging, after maxfilter)

* a description file ([#covdescription see below])

The end result is a fiff-file containing the noise covariance matrix, which can be read into Matlab using mne_read_noise_cov.

Note: For some applications, for example [http://imaging.mrc-cbu.cam.ac.uk/meg/AnalyzingData/MNE_singletrial single-trial analysis], you should use a covariance matrix computed on empty-room data. Pre-processing for these data should be as similar as possible to the raw data files used for analysis.

The parameters below are reasonable choices for standard analyses. However, these Wiki pages are not supposed to substitute the [http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.6.pdf MNE manual], [http://imaging.mrc-cbu.cam.ac.uk/meg/MEGpapers reading papers], and [http://imaging.mrc-cbu.cam.ac.uk/imaging/ImagersInterestGroup discussions] with more experienced researchers.

#


## Your variables:

datapath='<myrawMEGdatapath>'    # root directory for your MEG data

# MEG IDs (your directory structure may differ)
subj_pre=(\
        'meg10_0001' \
        'meg10_0002' \
        'meg10_0003' \
        )

# MEG subdirectories (your directory structure may differ)      
subj_dir=(\
         '100001' \
         '100002' \
         '100003' \
        )


## Processing:

nsubjects=${#subjects[*]}
lastsubj=`expr $nsubjects - 1`


for m in `seq 0 ${lastsubj}`
do
  echo " "
  echo " Computing covariance matrix for SUBJECT  ${subjects[m]}"
  echo " "

  mne_process_raw  \
                --raw ${datapath}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw1.fif \
                --raw ${datapath}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw2.fif \
                --raw ${datapath}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw3.fif \
                --eventsout ${datapath}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw1-eve.txt \
                --eventsout ${datapath}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw2-eve.txt \
                --eventsout ${datapath}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw3-eve.txt \         
                --projoff \
                --cov cov_desc1.cov \
                --cov cov_desc2.cov \
                --cov cov_desc3.cov \
                --savecovtag -cov \
                --gcov ${datapath}/${subj_pre[m]}/${subj_dir[m]}/covmat-cov.fif


done # subjects

Anchor(covdescription) where the covariance description files cov_desc?.cov are of the form

cov {
        gradReject      2000e-13
        magReject       3e-12
        eegReject       120e-6
        eogReject       150e-6
        logfile         YourLogFileName.txt

        def {
                event   1
                tmin    -0.2
                tmax    0
                basemin -0.2
                basemax 0.0
        }

        def {
                event   2
                tmin    -0.2
                tmax    0
                basemin -0.2
                basemax 0.0
        }


        def {
                event   3
                tmin    -0.2
                tmax    0
                basemin -0.2
                basemax 0.0
        }
        
}

If the parameters are the same for all input files, you only have to specify one description file. For more details and options see the MNE manual.