摘要
脑磁图作为一种新型的脑探测技术,具有较高定位精度和毫秒级时间分辨率的特点。快速准确地利用脑磁图技术对三维空间中的脑神经活动源进行定位,对于脑功能研究和医学临床应用都具有重要的应用价值。可是,目前的脑磁图源定位广泛采用了多信号分类方法,它要求对三维大脑空间进行全局扫描,需要大量的计算,存在速度慢的缺点。针对这一问题,提出了一种基于粒子群优化算法的脑磁图源定位方法。先利用粒子群优化算法全局搜索能力强的特点寻找出目标函数的全局最优值,进行初步的脑磁图源定位;然后,再在小范围内进行小网格的搜索,进一步实现精确的定位。实验结果表明,基于粒子群优化算法的脑磁图源定位能够很好地解决上述问题,具有计算速度快、定位精度高的特点。
The estimation of three-dimension neural activation sources from the magnetoencephalography （MEG） record is a very critical issue for both clinical neurology and brain functions research. Nowadays multiple signal classification （MUSIC） algorithm and recursive MUSIC algorithm are widely used to localize dipolar sources from the MEG data. The drawback of these two algorithms is that they need excessive calculation and is quite time-consuming when scanning a three-dimensional space. In order to solve this problem, the authors proposed an improved Particle Swarm Optimization （PSO）-based sources localization scheme here. This scheme used the property of global optimum of PSO to estimate the rough source locations. Then, combined with grids search in small area, the accurate dipolar source localization was performed. In addition, comparing the results of this method with those of the method based on Genetic Algorithm （GA）, computer simulation results show that this PSO strategy is an effective and precise approach to dipole localization which can improve the speed greatly and localize the sources accurately.
出处
《生物物理学报》
CAS
CSCD
北大核心
2008年第4期308-314,共7页
Acta Biophysica Sinica
基金
国家自然科学基金项目(60672116)
上海市重点学科建设项目(B112)~~
关键词
脑磁图
源定位
粒子群优化算法
Magnetoencephalography
Sources localization
Particle swarm optimization
