= 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) * '''[[#covdescription|a description file]]''' (see below) You can '''[[#visualisecov|visualise the covariance matrices]]''' in Matlab. 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 [[CbuImaging:ImagersInterestGroup|discussions]] with more experienced researchers. {{{ #!/bin/sh # ## Your variables: datapath='' # 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=${#subj_pre[*]} lastsubj=`expr $nsubjects - 1` for m in `seq 0 ${lastsubj}` do echo " " echo " Computing covariance matrix for SUBJECT ${subj_pre[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 \ --projon \ --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 }}} <> == Covariance Matrix Description File == where the '''covariance description files''' cov_desc?.cov are of the form {{{ cov { gradReject 200e-12 # artefact rejection thresholds magReject 3e-12 eegReject 120e-6 eogReject 150e-6 logfile YourLogFileName.txt # logfile that will contain some useful information fixSkew # Fixes problems with trigger onsets on different bits 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. <> == Visualise Covariance Matrices == The following script allows you to '''visualise covariance matrices''' for EEG (if used), magnetometers and gradiometers separately: {{{ % Will read MNE covariance matrix files (*.fif) % and visualised them separately for magnetometers, gradiometers and EEG % EEG will be average-referenced % Olaf Hauk, Nov 2010 covpath = '/YourPathToData/'; % root directory for data covname = 'YourFilename.fif'; % filename for MNE covariance matrix files in subject directories % Specify subject information: MEG ID 1, MEG ID2 (may depend on your experiment) % the script will look for covpath/subject{x}{1}/subject{x}{2}/covname as covariance matrix cnt = 1; subject{cnt} = {'meg10_0005', '123456'}; cnt = cnt+1; subject{cnt} = {'meg10_0006', '654321'}; cnt = cnt+1; subject{cnt} = {'meg10_0007', '162534'}; cnt = cnt+1; nr_subs = length(subject); % number of subjects specified for ss = 1:nr_subs, % for all subjects... filein = fullfile( covpath, subject{ss}{1}, subject{ss}{2}, covname ); % input filename of covariance matrix file fprintf(1, '\nReading covariance matrix from %s\n', filein); covmat = mne_read_noise_cov( filein ) % read noise covariance matrix using MNE Matlab tool channames = covmat.names; % names of channels indices_EEG = strmatch('EEG', channames); % indices for EEG channels indices_MEG = strmatch('MEG', channames); % indices for MEG channels (grads+mags) for i=1:length(indices_MEG), lastnum(i) = str2num( channames{indices_MEG(i)}(end) ); % get last number of MEG channel names end; % i indices_mags = indices_MEG( find ( lastnum == 1 ) ); % find magnetometer indices indices_grads = indices_MEG( find ( (lastnum==2)+(lastnum==3) ) ); % find gradiometer indices fprintf(1, 'There are %d magnetometers and %d gradiometers.\n', length(indices_mags), length(indices_grads)); figure; % create new figure if ~isempty(indices_EEG), % if file contains EEG... nr_EEG = length(indices_EEG); % number of electrodes fprintf(1, '...oh, and %d EEG electrodes.\n Average referencing EEG.\n\n', nr_EEG); covmatEEG = covmat.data(indices_EEG, indices_EEG); % separate EEG covariance matrix avgop = eye(nr_EEG) - ones(nr_EEG)/nr_EEG; % average reference operator covmatEEG = avgop*covmatEEG*avgop; % apply average reference to EEG covariance matrix nr_plots = 3; % plot mags, grads and EEG subplot(1,nr_plots,1); imagesc( covmatEEG ); axis( 'square' ); colorbar; th = title(['EEG ' subject{ss}{1}]); set(th, 'Interpreter', 'none'); else, nr_plots = 2; end; % plot mags and grads (no EEG) covmatmag = covmat.data(indices_mags, indices_mags); % separate mags covariance matrix subplot(1,nr_plots,2); imagesc( covmatmag ); axis( 'square' ); colorbar; th = title(['Mags ' subject{ss}{1}]); set(th, 'Interpreter', 'none'); covmatgrad = covmat.data(indices_grads, indices_grads); % separate grads covariance matrix subplot(1,nr_plots,3); imagesc( covmatgrad ); axis( 'square' ); colorbar; th = title(['Grads ' subject{ss}{1}]); set(th, 'Interpreter', 'none'); end; % ss }}} The result may look something like this: {{attachment:Covariance.jpg}} Note: The patterns in these images are due to the spatial arrangements of electrodes and sensors. They may therefore vary for different sensor configurations. The main thing to look out for are channels with huge covariances, or flat channels, or patterns that suggest that all electrodes measured the same signal (indicating a problem with the reference etc.). The EEG in this example was average-referenced, and the MEG data maxfiltered.