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.
|Title of host publication||IEEE International Workshop on Machine Learning for Signal Processing, 2009. MLSP 2009|
|Publication status||Published - 2009|
|Event||2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France|
Duration: 2 Sep 2009 → 4 Sep 2009
|Workshop||2009 IEEE International Workshop on Machine Learning for Signal Processing|
|Period||02/09/2009 → 04/09/2009|