Improved source estimation in EEG with Bayesian modelling of the unknown skull conductivityMS72

The estimation and visualization of active parts of the brain from EEG recordings is referred to as the EEG source imaging. The source estimation is highly sensitive to modeling uncertainties of the computational model, especially the electrical properties of the head tissues. We show that the exact knowledge of the skull conductivity is not always necessary, since it can be taken into account statistically in the inversion by using the Bayesian approximation error approach.

This presentation is part of Minisymposium “MS72 - Inverse problems with imperfect forward models (2 parts)
organized by: Yury Korolev (University of Cambridge) , Martin Burger (University of Muenster) .

Alexandra Koulouri (Aristotle University of Thessaloniki)
bayesian methods, image reconstruction, inverse problems