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* New method of Multiple Sparse Priors (MSP) (NB: not yet accepted): attachment:FristonEtAl_inpress.pdf  * New method of Multiple Sparse Priors (MSP): attachment:FristonEtAl_inpress.pdf 
Analysis of MEG Data in SPM5
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, timefreq analysis, 3D statistical maps), with proper stepbystep instructions via the GUI, see: http://www.mrccbu.cam.ac.uk/~rh01/analysis.html
 For a more theoretical introduction to source localisation in SPM5, see these slides: attachment:spm5_meg_wiki.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; odd math typo not corrected): attachment:HensonEtAl_NI_inpress.pdf
Use of inversenormalised canonical meshes: attachment:MattoutEtAl_JCIN_07.pdf
New method of Multiple Sparse Priors (MSP): attachment:FristonEtAl_inpress.pdf
General advice:
 First you will probably want to run your raw data through Max Filter, particularly if you 1) used Active Shielding during acquisition, 2) if you want to apply SSS to remove noise, 3) if you used continuous HPI. Max Filter can also downsample and convert the data into different datatypes (e.g, short).(Note that Matlab will have memory problems if you try to read in data of more than approx 10mins (at 1kHz), so downsampling to ~200300Hz will help.)
 Next you will need to convert your *.FIF files into Matlab and SPM format. For those using SPM5 at the CBU, this is now an option on the SPM5 GUI "convert" button (when in "EEG" mode) (utilising the function spm_eeg_rdata_FIF.m in /cbu_updates). Then you can perform averaging, filtering and other preprocessing in SPM, as well as distributed source localisation.