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