Stahlhut, Carsten3; Attias, Hagai Thomas4; Stopczynski, Arkadiusz2; Petersen, Michael Kai3; Larsen, Jakob Eg5; Hansen, Lars Kai5
1 Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 Department of Applied Mathematics and Computer Science, Technical University of Denmark4 Convex Imaging5 Copenhagen Center for Health Technology, Center, Technical University of Denmark
EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup’s dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.
Ieee Engineering in Medicine and Biology Society Conference Proceedings, 2012, p. 1538-1541
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I E E E Engineering in Medicine and Biology Society. Conference Proceedings
34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012