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The Scientific World Journal
Volume 2014 (2014), Article ID 524367, 9 pages
http://dx.doi.org/10.1155/2014/524367
Research Article

Multicompare Tests of the Performance of Different Metaheuristics in EEG Dipole Source Localization

1Information Technology Laboratory, Center for Research and Advanced Studies (Cinvestav), Ciudad Victoria, TAMPS 87130, Mexico
2Biomedical Signal Processing Laboratory, Center for Research and Advanced Studies (Cinvestav), Apodaca, NL 66600, Mexico

Received 20 December 2013; Accepted 10 February 2014; Published 16 March 2014

Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang

Copyright © 2014 Diana Irazú Escalona-Vargas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization’s performance in terms of metaheuristics’ operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics’ performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.