Particle swarm optimization for MEG source localization
datasetposted on 2017-11-21, 00:23 authored by Jiang, Chenwei, Wang, Bin, Zhang, Liming
The estimation of three-dimension neural active 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 locate dipolar sources from MEG data. The drawback of these algorithms is that they need excessive calculation and is quite time-consuming when scanning a three-dimensional space. In order to solve this problem, we propose a MEG sources localization scheme based on an improved Particle Swarm Optimization (PSO). This scheme uses the advantage of global searching ability of PSO to estimate the rough source location. Then combining with grids search in small area, the accurate dipolar source localization is performed. In addition, we compare the results of our method with those based on Genetic Algorithm (GA). Computer simulation results show that our PSO strategy is an effective and precise approach to dipole localization which can improve the speed greatly and localize the sources accurately. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Bioinformatics -- CongressesComputational biology -- CongressesComputer vision in medicine -- CongressesComputational biology -- Methods -- CongressesPattern recognition, automated -- Methods -- Congresses2008conference paper1959.1/63691monash:7853Bioinformatics SoftwareBioinformaticsPattern Recognition and Data Mining