= 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 (which are 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.

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.

{{{
#!/bin/sh
#


## 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.