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