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MEG and EEG Data Analysis Using MNE Software
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Basics
MEG/EEG data analysis in [http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php MNE software] uses information from structural [wiki:ImagingSequences MRI] images, which have to be pre-processed using [http://surfer.nmr.mgh.harvard.edu/ Freesurfer]. You may want to start with the tutorial based on an example data set, as described in the MNE manual ([attachment:MNE_V2.6.pdf Version 2.6], [attachment:MNE_V2.7.pdf Version 2.7.1]; [attachment:MNE_V2.7.3.pdf Version 2.7.3]; chapter 12), or look at [http://www.martinos.org/mne/ some example scripts]. Freesurfer is accompanied by extensive [http://surfer.nmr.mgh.harvard.edu/fswiki Freesurfer Wiki pages], containing a [http://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferBeginnersGuide Getting Started] and [http://surfer.nmr.mgh.harvard.edu/fswiki/UserContributions/FAQ FAQ] section. You will need some experience with Linux commands and scripting, which you may find on our [wiki:meg:Beginners beginners' pages].
If you've never used shell scripts before, this [wiki:AnalyzingData/Primer_ShellScripting primer on shell scripting] will get you on the way.
There is also a short description on how to [wiki:AnalyzingData/MNE_prepare prepare for MNE analysis and access the Matlab toolbox].
Look here for [http://mne-tools.github.com/mne-python-intro/ MNE Python tools], e.g. for time-frequency analysis and sensor-space statistics.
The parameters in the following examples are reasonable choices for standard analyses. However, these Wiki pages are not supposed to substitute the MNE manual ([http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.6.pdf V2.6], [http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.7.pdf V 2.7]), [wiki:MEGpapers reading papers], and [wiki:ImagersInterestGroup discussions] with more experienced researchers. You may also want to subscribe to the [http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis MNE mailing list].
Step-by-step Guide
Note that some of these steps can be done in parallel, for example MRI preprocessing and MEG averaging.
1) [wiki:AnalyzingData/MNE_MRI_preprocessing Pre-process your MRI Data Using Freesurfer]
2) [wiki:AnalyzingData/MNE_FixingFIFF Fix EEG electrode positions in Fiff-files]
3) [wiki:AnalyzingData/MNE_MRI_processing Create Source Space and Head Surfaces] (incl. aligning coordinate systems)
4) [wiki:AnalyzingData/MNE_ForwardSolution Compute the Forward Solution and BEM]
5) [wiki:AnalyzingData/MNE_CovarianceMatrix Compute the Noise Covariance Matrix]
6) [wiki:AnalyzingData/MNE_InverseOperator Compute the Inverse Operator]
7) [wiki:AnalyzingData/MNE_Averaging Averaging MEG data] (incl. correcting EEG location information, Marking bad channels)
8) [wiki:AnalyzingData/MNE_ComputeEstimates Compute the Source Estimates] (incl. average cortical surface, grand-averaging)
9) [wiki:AnalyzingData/MNE_Labels ROI/Label analysis] (incl. pre-defined labels, make-your-own)
All-in-One
[wiki:AnalyzingData/MNE_AllInOne List of Most Relevant MNE Commands]
Related Issues
1) You may want to [wiki:PreProcessing filter] or [wiki:Maxfilter maxfilter] ([wiki:MaxfilterMatlabScript Matlab script]) your data before averaging
2) At the moment, MNE does not provide any statistics tools (but see MNE-Python tools, point 11). You can use [wiki:SensorStats sensor stats] implemented in SPM ([wiki:SensorSpm SensorSPM]) for statistics in sensor space.
3) For [wiki:SensorSpm SensorSPM] ([http://imaging.mrc-cbu.cam.ac.uk/meg/SensorStats sensor stats]), you should [wiki:InterpolateData interpolate your MEG data] on a [wiki:StandardSensorArray standard sensory array].
4) For data exploration or visualisation, you may want to compute [wiki:GrandMean grand average data in signal space].
5) Applying the inverse operator to [wiki:AnalyzingData/MNE_singletrial single-trial data] requires some extra processing steps.
6) [wiki:AnalyzingData/MNE_simulation Simulate] your own data in MNE, e.g. to check localisation accuracy for specific ROIs
7) Compute [wiki:AnalyzingData/MNE_sensitivity Sensitivity Maps] for EEG and MEG configurations
8) [wiki:AnalyzingData/MNE_BaselineCorrectSTC Baseline Correction] for source estimates
9) [wiki:AnalyzingData/MNE_Vertices2MNI Converting vertex locations] from MNE STC-files to MNI coordinates
10)[wiki:AnalyzingData/MNE_SampleDataSet The MNE Sample Data Set] (CBU only)
11) [wiki:AnalyzingData/MNE_Python_CBU MNE Python tools] and [https://martinos.org/mne/auto_examples/ example scripts] (e.g. averaging, time-frequency analysis, non-parametric statistics)
Dan's Pages (from Martinos Center for Biomedical Imaging)
[wiki:MEG_Data_Processing MEG Data Processing]
[wiki:DanStructurals Structural Analysis]