Analysis of Alpha Rhythm Electroencephalogram Based on Inner Composition Alignment Algorithm

Yi-Yi HE, Dan-Qin XING, Jia-Qin WANG, Jia-Fei DAI, Jun WANG, Feng-Zhen HOU

Abstract


To research on brain function network, we must firstly figure out the structure of the brain. Brain connection diagram can be drawn up on the levels of macro and microscopic scale. This paper applied inner composition alignment (iota) algorithm to construct alpha rhythm brain functional network and visualize the network topology and analysis and the difference between the old and young’s brain functional connectivity. The results show that clustering coefficient of the old brain network obviously differs from the young by calculating t testing with spss software, which proved that clustering coefficient of the old alpha rhythm, differs from the young.

Keywords


Inner Composition Alignment, Alpha Rhythm, Electroencephalogram


DOI
10.12783/dtbh/mshh2017/13446

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