Title
Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE) Using a Hierarchical Bayesian Approach
Abstract
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.
Year
DOI
Venue
2011
10.1007/s11265-010-0527-0
Signal Processing Systems
Keywords
Field
DocType
EEG,Inverse problem,Source localization,Distributed models,Variational Bayes,Forward model reconstruction
Source reconstruction,Model representation,Computer science,Source localization,Artificial intelligence,Inverse problem,Eeg data,Machine learning,Electroencephalography,Bayesian probability,Model reconstruction
Journal
Volume
Issue
ISSN
65
3
1939-8018
Citations 
PageRank 
References 
5
0.46
12
Authors
4
Name
Order
Citations
PageRank
Carsten Stahlhut1558.47
Morten Mørup270451.29
Winther, Ole3960106.57
Lars Kai Hansen42776341.03