Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2009

View graph of relations

In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, 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 model by comparison with source reconstruction methods that use fixed forward models. Simulated and real EEG data demonstrate that invoking a stochastic forward model leads to improved source estimates.
Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009
PublisherIEEE
Publication date2009
Pages1-6
ISBN (Print)978-1-4244-4947-7
DOIs
StatePublished - 2009
Event2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France
Duration: 2 Sep 20094 Sep 2009
http://mlsp2009.conwiz.dk/

Workshop

Workshop2009 IEEE International Workshop on Machine Learning for Signal Processing
CountryFrance
CityGrenoble
Period02/09/200904/09/2009
Internet address

Bibliographical note

Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

CitationsWeb of Science® Times Cited: No match on DOI
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

Download statistics

No data available

ID: 4592190