Application and Comparison of Some Nonlinear Optimization Methods in EEG Inverse Problem

This paper discusses EEG dipole source localization problems solved by nonlinear optimization methods, such as Simplex, Levenberg-Marquart and Hybrid Genetic Algorithm. Computer simulation demonstrates that these algorithms are all available under certain conditions. In comparison, Simplex, LM algorithms are of fast computation speed while HGA costs more time. On the other hand, HGA almost has no special need for the selection of initial iterative values while Simplex and LM algorithms haves such need to some extent. Furthermore, on estimation of multiple source parameters, Simplex, LM algorithms sometimes fail due to incorrect selection of initial iterative values, while HGA is still effective.