The localization of dipolar sources in the brain based on electroencephalography (EEG) or magnetoencephalography (MEG) data is a frequent problem in the neurosciences. Deterministic standard approaches such as the Levenberg-Marquardt (LM) method often have problems in finding the global optimum of the associated nonlinear optimization function, when two or more dipoles are to be reconstructed. In such cases, probabilistic approaches turned out to be superior, but their applicability in neuromagnetic source localizations is not yet satisfactory. The objective of this study was to find probabilistic optimization strategies that perform better in such applications. Thus, hybrid and nested evolution strategies (NES) which both realize a combination of global and local search by means of multilevel optimizations were newly designed. The new methods were bench-marked and compared to the established evolution strategies (ES), to fast evolution strategies (FES), and to the deterministic LM method by conducting a two-dipole fit with MEG data sets from neuropsychological experiments. The best results were achieved with NES.
['Roland Eichardt', 'Jens Haueisen', 'Thomas R. Knösche', 'Ernst Günter Schukat-Talamazzini']
Reconstruction of Multiple Neuromagnetic Sources Using Augmented Evolution Strategies— A Comparative Study