Christian Engwer: Bridging the gap between Numerical Analysis and Brain Research

Brain source analysis is an important tool in brain research. It is used for example during operation planning 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, that 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 methods like discontinuous Galerkin (dG) methods and cut-cell techniques can increase the robustness of the forward problem and simplify the overall workflow. Hardware-oriented design of numerical methods allows for improved speed of the inverse simulation by making use of modern hardware resources. In order to compute the forward problem efficiently, we propose an algebraic multigrid solver for cut-cell dG methods. We introduce the challenges of EEG/MEG inverse modeling and discuss how different parts of the problem can be improved using modern numerical methods.

Hybrid format via Zoom possible on demand (contact timokoch at uio.no)

The talk will start at ca. 13:00 right after the talk by Andrea Bressan.