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| SPM5 can perform basic preprocessing of MEG data, and source reconstruction, including ECD and variants of L2-norm imaging solutions. It's weaknesses are: i) ECD is limited (e.g, the Neuromag Xfit program is more flexible), ii) it's graphics are slow. It's strengths are: i) well integrated with MRI and fMRI analysis (eg, segmentation), ii) in Matlab, so relatively easy to understand and extend, iii) includes advanced statistical methods (e.g, for multiple comparisons across space or time), and novel variants of the L2-norm approach to the inverse problem, iv) includes group-based analysis (eg in MNI/Talairach space). | Some general advice (for specific demo, see SpmDemo): |
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| For an introduction, see these slides: RiksSlides | 1. First you will probably need 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 ~200-300Hz will help.) 1. 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. * For a fuller demo of EEG/MEG analysis in SPM5, including more general features (e.g, time-freq analysis, 3D statistical maps), with proper step-by-step instructions via the GUI (though not on FIF data from our machine), see: http://www.mrc-cbu.cam.ac.uk/~rh01/analysis.html * For a more theoretical introduction to source localisation in SPM5, see these slides: attachment:spm5_meg_wiki.ppt Some papers on SPM's approach will appear here soon... |
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| First, you will need to convert your *.FIF files into SPM files. | ''To be continued...'' |
Analysis of MEG Data in SPM5
Some general advice (for specific demo, see SpmDemo):
- First you will probably need 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 ~200-300Hz 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.
For a fuller demo of EEG/MEG analysis in SPM5, including more general features (e.g, time-freq analysis, 3D statistical maps), with proper step-by-step instructions via the GUI (though not on FIF data from our machine), see: http://www.mrc-cbu.cam.ac.uk/~rh01/analysis.html
- For a more theoretical introduction to source localisation in SPM5, see these slides: attachment:spm5_meg_wiki.ppt
Some papers on SPM's approach will appear here soon...
To be continued...