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作 者:SHAO Chenxi SHEN Linfeng WANG Xufa and YIN Shijie(1. Department of Computer Science & Technology, University of Science & Technology of China, Hefei 230027, China 2. Department of Neurology, University of Medical Science of Anhui, Hefei 230022, China)
出 处:《Progress in Natural Science:Materials International》2002年第12期915-919,共5页自然科学进展·国际材料(英文版)
基 金:the National Natural Science Foundation of China (Grant No. 69974038) ; National High Performance Computing Foundation (Grant No. 00215)
摘 要:The nonlinear analysis is used to study the EEGs of the alcoholic and the control. Three kinds of expansions are discussed in order to get a more proper delay and then Liangyue Cao algorithm is implemented efficiently. Totally, there are 40 subjects involved in this study and the average values and the sample variances of D2s are computed. The results show that the average value of D2s of the alcoholic is larger than that of the control when the same electrode was used, which means that the brain dynamics of the alcoholic is more complex than that of the control. On the other hand, for most of the electrodes, the sample variance of D2s of the alcoholic is larger than that of the control, suggesting that the brain dynamics of the alcoholic is less steady.The nonlinear analysis is used to study the EEGs of the alcoholic and the control. Three kinds of expansions are discussed in order to get a more proper delay and then Liangyue Cao algorithm is implemented efficiently. Totally, there are 40 subjects involved in this study and the average values and the sample variances of D2s are computed. The results show that the average value of D2s of the alcoholic is larger than that of the control when the same electrode was used, which means that the brain dynamics of the alcoholic is more complex than that of the control. On the other hand, for most of the electrodes, the sample variance of D2s of the alcoholic is larger than that of the control, suggesting that the brain dynamics of the alcoholic is less steady.
关 键 词:EEG alcoholic NONLINEAR dynamics complexity stability.
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