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

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.
Keywords	EEG - Inverse problem - Source localization - Distributed models - Variational Bayes - Forward model reconstruction
Type	Journal paper [With referee]
Journal	Journal of Signal Processing Systems
Year	2010
Publisher	Springer New York
Publication link	http://dx.doi.org/10.1007/s11265-010-0527-0
BibTeX data	[bibtex]
IMM Group(s)	Intelligent Signal Processing