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Bridging the gap between numerical analysis and brain research
27. November 2019 @ 14:00 - 15:30
Prof. Dr. Christian Engwer
Brain source analysis is an important tool in brain research. It is used for example during operation planing
for epilepsy patients. Given EEG (electroencephalography) and MEG (magnetoencephalography)
measurements the goal is to reconstruct the brain activity, i.e. the electric potential in the brain. This
poses an inverse problem. It was observed in experiments, the accuracy of the inverse problem strongly
depends on the quality of the forward simulation, in particular the head model. We discuss how modern
numerical method like discontinuous Galerkin (dG) methods and cut-cell techniques can increase
robustness of the forward problem and simplify the overall work-fl ow. Hardware oriented design of numerical
methods allow to improve speed of the inverse simulation by making use of modern hardware
resources. In order to compute the forward problem effi ciently we propose an algebraic multigrid solver
for cut-cell dG methods. We introduce into the challenges of EEG/MEG inverse modelling and discuss
how diff erent parts of the problem can be improved using modern numerical methods.