Multiple Signal Classification Based on Chaos Optimization Algorithm for MEG Sources Localization

How to localize the neural activation sources effectively and precisely from the magnetoencephalographic (MEG) recording is a critical issue for the clinical neurology and the study on brain functions. Multiple signal classification (MUSIC) algorithm and its extension referred to as recursive MUSIC...

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Bibliographic Details
Published inAdvances in Neural Networks - ISNN 2006 pp. 600 - 605
Main Authors Ma, Jie-Ming, Wang, Bin, Cao, Yang, Zhang, Li-Ming
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
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Summary:How to localize the neural activation sources effectively and precisely from the magnetoencephalographic (MEG) recording is a critical issue for the clinical neurology and the study on brain functions. Multiple signal classification (MUSIC) algorithm and its extension referred to as recursive MUSIC algorithm are widely used to localize 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 source localization method based on chaos optimization algorithm is proposed. This method uses the property of ergodicity of chaos to estimate the rough source location. Then combining with grids in small area, the accurate dipolar source localization is performed. Experimental results show that this method can improve the speed of source localization greatly and its accuracy is satisfactory.
Bibliography:This research was supported by the grant from the National Natural Science Foundation of China (No. 30370392).
ISBN:9783540344827
3540344829
ISSN:0302-9743
1611-3349
DOI:10.1007/11760191_88