This script applies the inverse operator to MEG data and outputs the current estimates. The current estimates are morphed to the average brain (see below), for grand-averaging. The results (*.stc-files) can be viewed in mne_analyze.

The main ingredients are

* the inverse operator

* the MEG data (fiff-files)

* the average cortical surface (see below)

For more details and further options see the MNE manual.

#

## Your variables

path='<myMEGdatapath>'    # where your MEG fiff-files are
MRIpath='/myMRIdirectory/'    # where your MRI subdirectories are
outpath='/myoutpath'  # path for output files

#condition names as used in file names to which inverse operator shall be applied
conds=('cond1' 'cond2' 'cond3')


# subjects names used for MRI data
subjects=(\
        'Subject1' \
        'Subject1' \
        'Subject1' \
)

# 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' \
        )




#####################
# SCRIPT BEGINS HERE:

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

nconds=${#conds[*]}
lastcond=`expr $nconds - 1`


for m in `seq 0 ${lastsubj}`
do
  echo " "
  echo " Computing movies  for SUBJECT  ${subjects[m]}"
  echo " "
 
        for c in `seq 0 ${lastcond}`
        do
        
                # Current Estimates
                mne_make_movie \
                --subject {subjects[m]} \
                --inv ${path}/${subj_pre[m]}_0${subjects[m]}/${subj_dir[m]}/YourName_1L-MEG-loose0.2-inv.fif \
                --meas ${path}/${subj_pre[m]}/${subj_dir[m]}/${conds[c]}.fif \
                --morph average \
                --smooth 5 \
                --bmin -100 \
                --bmax 0 \
                --stc ${outpath}/STC/${subj_pre[m]}_${conds[c]}
                        
        done # conditions

done # subjects