= MEG and EEG Data Analysis Using MNE Software =

attachment:MNE_title.jpg

== Basics ==
MEG/EEG data analysis in [http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php MNE software] uses information from structural [wiki:CbuImaging: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  ([http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.6.pdf Version 2.6] or [http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.7.pdf Version 2.7]; 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:CbuMeg:AnalyzingData/Primer_ShellScripting primer on shell scripting] will get you on the way.

There is also a short description on how to [wiki:CbuMeg: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:CbuMeg:MEGpapers reading papers], and [wiki:CbuImaging: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:CbuMeg:AnalyzingData/MNE_MRI_preprocessing Pre-process your MRI Data Using Freesurfer]

2) [wiki:CbuMeg:AnalyzingData/MNE_MRI_processing Create Source Space and Head Surfaces] (incl. aligning coordinate systems)

3) [wiki:CbuMeg:AnalyzingData/MNE_ForwardSolution Compute the Forward Solution and BEM]

4) [wiki:CbuMeg:AnalyzingData/MNE_CovarianceMatrix Compute the Noise Covariance Matrix]

5) [wiki:CbuMeg:AnalyzingData/MNE_InverseOperator Compute the Inverse Operator]

6) [wiki:CbuMeg:AnalyzingData/MNE_Averaging Averaging MEG data] (incl. correcting EEG location information, Marking bad channels)

7) [wiki:CbuMeg:AnalyzingData/MNE_ComputeEstimates Compute the Source Estimates] (incl. average cortical surface, grand-averaging)

8) [wiki:CbuMeg:AnalyzingData/MNE_Labels ROI/Label analysis] (incl. pre-defined labels, make-your-own)

== All-in-One ==
[wiki:CbuMeg:AnalyzingData/MNE_AllInOne List of Most Relevant MNE Commands]

== Related Issues ==
1) You may want to [wiki:CbuMeg:PreProcessing filter] or [wiki:CbuMeg:Maxfilter maxfilter] ([wiki:CbuMeg: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:CbuMeg:SensorStats sensor stats] implemented in SPM ([wiki:CbuMeg:SensorSpm SensorSPM]) for statistics in sensor space.

3) For [wiki:CbuMeg:SensorSpm SensorSPM] ([http://imaging.mrc-cbu.cam.ac.uk/meg/SensorStats sensor stats]), you should [wiki:CbuMeg:InterpolateData interpolate your MEG data] on a [wiki:CbuMeg:StandardSensorArray standard sensory array].

4) For data exploration or visualisation, you may want to compute [wiki:CbuMeg:GrandMean grand average data in signal space].

5) Applying the inverse operator to [wiki:CbuMeg:AnalyzingData/MNE_singletrial single-trial data] requires some extra processing steps.

6) [wiki:CbuMeg:AnalyzingData/MNE_simulation Simulate] your own data in MNE, e.g. to check localisation accuracy for specific ROIs

7) Compute [wiki:CbuMeg:AnalyzingData/MNE_sensitivity Sensitivity Maps] for EEG and MEG configurations

8) [wiki:CbuMeg:AnalyzingData/MNE_BaselineCorrectSTC Baseline Correction] for source estimates

9) [wiki:CbuMeg:AnalyzingData/MNE_Vertices2MNI Converting vertex locations] from MNE STC-files to MNI coordinates

10)[wiki:CbuMeg:AnalyzingData/MNE_SampleDataSet The MNE Sample Data Set] (CBU only)

11) [http://mne-tools.github.com/mne-python-intro/ 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:CbuMeg:MEG_Data_Processing MEG Data Processing]

[wiki:CbuImaging:DanStructurals Structural Analysis]