Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE) using a Hierarchical Bayesian Approach

Publication: Research - peer-reviewJournal article – Annual report year: 2010

View graph of relations

We present an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model representation is motivated by the many random contributions to the path from sources to measurements including the tissue conductivity distribution, the geometry of the cortical surface, and electrode positions. We first present a hierarchical Bayesian framework for EEG source localization that jointly performs source and forward model reconstruction (SOFOMORE). Secondly, we evaluate the SOFOMORE approach by comparison with source reconstruction methods that use fixed forward models. Analysis of simulated and real EEG data provide evidence that reconstruction of the forward model leads to improved source estimates.
Original languageEnglish
JournalJournal of Signal Processing Systems
Publication date2011
Volume65
Issue3
Pages431-444
ISSN1939-8018
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 0

Keywords

  • Inverse problem, Distributed models, Variational Bayes, Forward model reconstruction, EEG, Source localization
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

ID: 6243625