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

Authors:
Carsten Stahlhut;Morten Mørup;Ole Winther;Lars Kai Hansen
Affiliations:
Department of Informatics and Mathematical Modelling, Technical University of Denmark, Kgs. Lyngby, Denmark 2800;Department of Informatics and Mathematical Modelling, Technical University of Denmark, Kgs. Lyngby, Denmark 2800;Department of Informatics and Mathematical Modelling, Technical University of Denmark, Kgs. Lyngby, Denmark 2800;Department of Informatics and Mathematical Modelling, Technical University of Denmark, Kgs. Lyngby, Denmark 2800
Venue:
Journal of Signal Processing Systems
Year:
2011

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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.