首页> 外文会议>Advances in Neural Networks - ISNN 2007 pt.2; Lecture Notes in Computer Science; 4492 >Multiple Signal Classification Based on Genetic Algorithm for MEG Sources Localization
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Multiple Signal Classification Based on Genetic Algorithm for MEG Sources Localization

机译:基于遗传算法的MEG源定位多信号分类

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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.
机译:如何有效地,准确地从脑磁图(MEG)记录中定位神经激活源是临床神经病学和脑功能研究的关键问题。多信号分类(MUSIC)算法和递归MUSIC算法被广泛用于从MEG数据中定位多个偶极子源。这些算法的缺点是,在全局扫描三维头部体积时,它们的运行速度非常慢。为了解决这个问题,提出了一种基于遗传算法的新型MEG源定位方案。首先,该方案利用遗传算法的全局最优性来估计粗糙源位置。然后,结合小面积的栅格,执行精确的偶极子源定位。此外,我们介绍了自适应交叉和变异概率,两点交叉算子,周期性替换和小生境策略,以克服遗传算法有时会陷入局部最优的缺点。实验结果表明,该方案可以极大地提高声源定位的速度,精度令人满意。

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