Journal of Signal Processing Systems

, Volume 65, Issue 3, pp 431–444 | Cite as

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

  • Carsten Stahlhut
  • Morten Mørup
  • Ole Winther
  • Lars Kai Hansen
Article

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 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Carsten Stahlhut
    • 1
  • Morten Mørup
    • 1
  • Ole Winther
    • 1
  • Lars Kai Hansen
    • 1
  1. 1.Department of Informatics and Mathematical ModellingTechnical University of DenmarkKgs. LyngbyDenmark

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