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

Carsten Stahlhut, Morten Mørup, Ole Winther, Lars Kai Hansen

AbstractIn 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.
KeywordsEEG, source reconstruction, stochastic forward model, hierarchical Bayes, distributed model
TypeConference paper [With referee]
ConferenceIEEE Workshop on Machine Learning for Signal Processing (MLSP)
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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