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* New method of Multiple Sparse Priors (MSP): attachment:FristonEtAl_inpress.pdf  * New method of Multiple Sparse Priors (MSP): attachment:FristonEtAl_NI_08_MSP.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.fil.ion.ucl.ac.uk/spm/data/mmfaces.html
 For a more theoretical introduction to source localisation in SPM5, see these slides: attachment:hensonSPMGrad084meeg.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 inversenormalised canonical meshes: attachment:MattoutEtAl_JCIN_07.pdf
New method of Multiple Sparse Priors (MSP): attachment:FristonEtAl_NI_08_MSP.pdf
Additional SPM5 functions:
If you are not using the version of SPM5 installed on the CBU network (ie do not have */spm5/cbu_updates on your Matlab path)  eg are an external collaborator  you will need to download the latest versions of some sitespecific SPM5 functions in order to read the FIF format data files from our MEG scanner. These SPM functions can be downloaded from here: http://www.mrccbu.cam.ac.uk/~rh01/fif2spm.html. (You may also want to email RikHenson in case there have been further changes since.)
General advice:
First you will probably 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 SSS to remove noise, 3) if you used continuous HPI. 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.
 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.