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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.

At the moment, we are using SPM5 (with any latest updates automatically pulled from the FIL, http://www.fil.ion.ucl.ac.uk/spm/).

  • 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.