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Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

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Authors:
  • Stahlhut, Carsten ;
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    Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Mørup, Morten ;
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    Orcid logo0000-0003-4985-4368
    Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Winther, Ole ;
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    Orcid logo0000-0002-1966-3205
    Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Hansen, Lars Kai
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    Orcid logo0000-0003-0442-5877
    Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark
DOI:
10.1109/mlsp.2009.5306189
Abstract:
In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, 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 model by comparison with source reconstruction methods that use fixed forward models. Simulated and real EEG data demonstrate that invoking a stochastic forward model leads to improved source estimates.
ISBN:
9781424449477
Type:
Conference paper
Language:
English
Published in:
Ieee International Workshop on Machine Learning for Signal Processing, 2009. Mlsp 2009, 2009, p. 1-6
Keywords:
Main Research Area:
Science/technology
Publication Status:
Published
Review type:
Peer Review
Conference:
2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009
Publisher:
IEEE
Submission year:
2009
Scientific Level:
Scientific
ID:
108157781

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