Conference paper
Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2009 |
| Pages: | 1-6 |
| Proceedings: | 2009 IEEE International Workshop on Machine Learning for Signal Processing |
| ISBN: | 1424449472 , 9781424449477 , 1424449480 and 9781424449484 |
| ISSN: | 21610363 and 15512541 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2009.5306189 |
| ORCIDs: | Mørup, Morten , Winther, Ole and Hansen, Lars Kai |
Bayesian methods Brain modeling Conductivity EEG source Electroencephalography Image reconstruction Inverse problems Magnetic resonance imaging Mathematical model Sensor arrays Uncertainty belief networks biological tissues cortical surface electrode positions electroencephalography forward model reconstruction forward propagation model hierarchical Bayesian model hierarchical systems medical signal processing signal reconstruction source reconstruction methods stochastic forward model stochastic processes tissue conductivity distribution
