Multiple Signal Classification Based on Genetic Algorithm for MEG Sources Localization

  • Authors:

  • Chenwei Jiang;Jieming Ma;Bin Wang;Liming Zhang

  • Affiliations:

  • Department of Electronics Engineering, Fudan University, Shanghai 200433, China;Department of Electronics Engineering, Fudan University, Shanghai 200433, China;Department of Electronics Engineering, Fudan University, Shanghai 200433, China and The Research Center for Brain Science, Fudan University, Shanghai 200433, China;Department of Electronics Engineering, Fudan University, Shanghai 200433, China and The Research Center for Brain Science, Fudan University, Shanghai 200433, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

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Abstract

How to locate the neural activation sources effectively and precisely from the magnetoencephalographic (MEG) recording is a critical issue for the clinical neurology and brain functions research. Multiple signal classification (MUSIC) algorithm and recursive MUSIC algorithm are widely used to locate multiple dipolar sources from the MEG data. The drawback of these algorithms is that they run very slowly when scanning a three-dimensional head volume globally. In order to solve this problem, a novel MEG sources localization scheme based on genetic algorithm (GA) is proposed. First, this scheme uses the property of global optimum of GA to estimate the rough source location. Then, combined with grids in small area, the accurate dipolar source localization is performed. Furthermore, we introduce the adaptive crossover and mutation probability, two-point crossover operator, periodical substitution and niche strategies to overcome the disadvantage of GA which falls into local optimum occasionally. Experimental results show that the proposed scheme can improve the speed of source localization greatly and its accuracy is satisfactory.