title: [Analysis of EEG based on the complexity measure].
abstract: EEG represents the electric activity of neurons in human brain; it is of course repeatedly used for studying and analyzing the brain activity and the status of brain function. In this paper, we analyzed the patients' and normal persons' EEG in different physiological state, with the aid of two algorithms as a complexity measure. One is Kc complexity defined by Kaspar and Schuster, the other is a new statistical method to measure complexity sequences-Approximate entropy (ApEn). In our work, we analyzed two groups of persons' EEG. Six subjects in 4 different experimental condition are reported. From the results we can discriminate the different state of brain effectively: normal, being injured, and various thinking state. The result suggests that the two algorithms as a complexity measure could be regarded as valued methods in the study of EEG time series and clinical diagnosis.
authors.affiliation: College of Bioengineering, Chongqing University, Chongqing 400044.
authors.forename: Pin
authors.initials: P
authors.lastname: Wang
authors.forename: Xiaolin
authors.initials: X
authors.lastname: Zheng
authors.forename: Chenglin
authors.initials: C
authors.lastname: Peng
authors.forename: Weiwei
authors.initials: W
authors.lastname: Dong
authors.forename: Yonghong
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D019277:Entropy; D006801:Humans