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= 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. {{{ |
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path='<myrawMEGdatapath>' # where your raw MEG fiff-files are | datapath='<myrawMEGdatapath>' # root directory for your MEG data |
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--raw ${path}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw1.fif \ --raw ${path}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw2.fif \ --raw ${path}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw3.fif \ --eventsout ${path}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw1-eve.txt \ --eventsout ${path}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw2-eve.txt \ --eventsout ${path}/${subj_pre[m]}/${subj_dir[m]}/rawMEGfile_raw3-eve.txt \ |
--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 \ |
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--cov GNG.cov \ | --cov cov_desc1.cov \ --cov cov_desc2.cov \ --cov cov_desc3.cov \ |
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--gcov ${path}/${subj_pre[m]}/${subj_dir[m]}/covmat-cov.fif | --gcov ${datapath}/${subj_pre[m]}/${subj_dir[m]}/covmat-cov.fif |
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}}} [[Anchor(covdescription)]] where the covariance description files cov_desc?.cov are of the form {{{ cov { gradReject 20e-15 # artefact rejection thresholds magReject 3e-12 eegReject 120e-6 eogReject 150e-6 logfile YourLogFileName.txt # logfile that will contain some useful information def { event 1 # trigger code tmin -0.2 # interval used for covariance computation tmax 0 basemin -0.2 # interval used for baseline correction 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. |
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 20e-15 # artefact rejection thresholds magReject 3e-12 eegReject 120e-6 eogReject 150e-6 logfile YourLogFileName.txt # logfile that will contain some useful information def { event 1 # trigger code tmin -0.2 # interval used for covariance computation tmax 0 basemin -0.2 # interval used for baseline correction 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.