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

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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.
    Original languageEnglish
    JournalJournal of Signal Processing Systems
    Volume65
    Issue number3
    Pages (from-to)431-444
    ISSN1939-8018
    DOIs
    Publication statusPublished - 2011

    Keywords

    • Inverse problem
    • Distributed models
    • Variational Bayes
    • Forward model reconstruction
    • EEG
    • Source localization

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