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Analysis of MEG Data in SPM5

Many of us here use SPM for EEG and/or MEG analysis. The main reason is that SPM is freeware (requiring only Matlab), and thus modifiable and relatively easily to understand (if you have basic knowledge of a procedural computer language, given that Matlab is a fairly high-level language). Several of us are actively extending SPM for our own purposes, particularly in relation to the Neuromag type of MEG data.


  • For specific demo using data from our Neuromag MEG machine, see SpmDemo

  • For a fuller demo of other EEG/MEG analysis in SPM5 (though from a different MEG machine), including more general features (e.g, time-freq analysis, 3D statistical maps), with proper step-by-step instructions via the GUI, see: http://www.fil.ion.ucl.ac.uk/spm/data/mmfaces.html

  • For a more theoretical introduction to source localisation in SPM5, see these slides: attachment:henson-SPM-Grad08-4-meeg.ppt


Here are some relevant papers:

  • Summary of localisation approach using ReML for evoked and induced responses (mathematical; cites earlier development papers too): attachment:FristonEtAl_hbm_06.pdf

  • Basic considerations for Group Analyses (though using individual meshes): attachment:HensonEtAl_NI_07.pdf

  • Use of inverse-normalised ("canonical") cortical meshes: attachment:MattoutEtAl_JCIN_07.pdf

  • Choice of forward models for MEG (e.g, single-sphere vs BEM), including further validation of canonical meshes: attachment:HensonEtAl_NI_inpress.pdf

  • New method of Multiple Sparse Priors (MSP): attachment:FristonEtAl_NI_08_MSP.pdf


Additional SPM5 functions:


General note:

  • SPM5 can read raw and averaged FIF files, though you will probably first want to run your raw data through the [:Maxfilter:Maxfilter utility], particularly if you 1) used Active Shielding during acquisition, 2) if you want to apply (temporal) SSS to remove noise, 3) if you used continuous HPI and/or 4) if you want to transform all subjects to a common (device) space. Max Filter can also downsample (eg from 1000Hz to 200Hz) and convert the data into different datatypes (e.g, short), which will help reduce filesize and processing time.