312013-03-08 10:02:15localhostconverted to 1.6 markup302012-11-27 23:21:17cpc6-cmbg14-2-0-cust458.5-4.cable.virginmedia.com292012-04-16 16:57:28YaaraErez282012-02-16 18:37:13YaaraErez272011-02-07 18:43:03YaaraErez262011-02-07 18:37:42YaaraErez252011-02-07 18:36:48YaaraErez242011-01-27 10:32:44YaaraErez232010-11-19 15:50:36YaaraErez222010-11-19 15:50:12YaaraErez212010-11-19 15:45:19YaaraErez202010-11-19 15:28:28YaaraErez192010-11-19 15:27:59YaaraErez182010-11-19 15:22:01YaaraErez172010-11-09 14:29:33YaaraErez162010-07-22 15:29:07YaaraErez152010-07-22 14:35:50YaaraErez142010-07-22 14:34:20YaaraErez132010-07-22 14:28:51YaaraErez122010-07-06 11:09:19YaaraErez112010-06-16 12:09:30YaaraErez102010-06-10 13:12:06YaaraErez92010-06-10 13:08:59YaaraErez82010-06-10 13:07:54YaaraErez72010-06-10 13:04:30YaaraErez62010-06-10 12:05:47YaaraErez52010-06-09 16:36:50YaaraErez42010-06-09 16:34:30YaaraErez32010-06-09 16:32:15YaaraErez22010-06-09 16:29:19YaaraErez12010-06-09 16:27:20YaaraErez

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 (see below) You can 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 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 MNE manual, reading papers, and discussions with more experienced researchers. ' # 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]]>

where the covariance description files cov_desc?.cov are of the form 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.

The following script allows you to visualise covariance matrices for EEG (if used), magnetometers and gradiometers separately: The result may look something like this: 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.