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SUMMARY:Hierarchical Bayesian Approaches to EEG/MEG Source Reconstruction 
 - Felix Lucka (University of Münster)
DTSTART:20121115T140000Z
DTEND:20121115T150000Z
UID:TALK40782@talks.cam.ac.uk
CONTACT:Carola-Bibiane Schoenlieb
DESCRIPTION:In the last talk\, I want to describe how hierarchical Bayesia
 n modeling (HBM) is used to address various challenges arising from the in
 verse problem of  Electroencephalography  (EEG) / Magnetoencephalography (
 MEG) based source reconstruction: Measuring the induced electromagnetic fi
 elds at the head surface to estimate the underlying\, activity-related ion
  currents in the brain is a challenging\, severely ill-posed inverse probl
 em. Due to the under-determinedness and the spatial characteristics of the
  direct problem\, especially the recovery of brain networks involving deep
 -lying sources is still a challenging task for any inverse method. Apart f
 rom these theoretical problems\, many practical challenges and uncertainti
 es arise in the analysis of EEG/MEG data. However\, the high temporal reso
 lution of EEG/MEG recordings and their direct correspondence to the neuron
 al activity makes them indispensable tools for neuroimaging. \nI want to s
 ketch how hierarchical Bayesian modeling (HBM) can be used as a convenient
  framework to address some of the above aspects and show own results on th
 e performance of fully-Bayesian inference methods for HBM for source confi
 gurations consisting of few\, focal sources when used with realistic\, hig
 h resolution Finite Element (FE) head models.\n\nSlides of this talk can b
 e downloaded at http://wwwmath.uni-muenster.de/num/burger/organization/luc
 ka/talks/Cambridge3_15_11_2012.pdf
LOCATION:CMS\, MR13
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