Music Localization for MEG Sources Based on Chaos Optimization Algorithm
基于混沌优化算法的MUSIC脑磁图源定位方法

How to localize the neural activitation sources effectively and precisely from the magnetoencephalographic recordings is a critical issue for the clinical neurology and the study on brain functions. Multiple signal classification algorithm and its extension which is referred to as recursive multiple signal classification algorithm are widely used to localize multiple dipolar sources from the magnetoencephalographic data. The shortage 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 magnetoencephalographic source localization method based on chaos optimization algorithm is proposed. This method uses the property of ergodicity of chaos to estimate the rough source locations as the arguments which are close to the global maximum of the cost function, then, combining with grids in small areas, the accurate dipolar source localization is performed. Experimental results show that this method can localize multiple dipolar sources easily. The speed of source localization can be improved greatly and the accuracy is satisfactory.