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 * 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.
* 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) (which utilises the function spm_eeg_rdata_FIF.m). Then you can perform averaging, filtering and other preprocessing in SPM, as well as distributed source localisation.

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, 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 meshes: attachment:MattoutEtAl_JCIN_07.pdf

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


Additional SPM5 functions:


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) (which utilises the function spm_eeg_rdata_FIF.m). Then you can perform averaging, filtering and other preprocessing in SPM, as well as distributed source localisation.

CbuMeg: SpmAnalysis (last edited 2013-03-08 10:02:27 by localhost)