摘要
构造了与脑电的源熵相同的（０，１）分布随机序列，以及用白噪激励依据脑电构造出的ＡＲ模型而得到的ＡＲ序列来作为伪脑电信号。通过比较这三种序列的复杂度，证明了脑电远非随机信号，而是存在某种模式，这种模式可以由ＡＲ模型部分表示出来。在此基础上，对三种精神状态下的脑电序列的复杂度进行了双因素方差分析，结果表明复杂度可以显著地区分这三种状态。
in this paper. two kinds of surrogate data are designed according to electroencephalography (EEG)data. They are a binomial random series with the same source entropy as that of EEG and an AR series that is theoutput of AR model constructed from EEG data and driven by white noise. Significant differences among thecomplexity of these three series show that BEG is far from being random and has certain pattern in it which canbe partially modeled by AR. The complexity of EEG series recorded during three different mental states iscomputed and the result of two-way analysis of variance shows that the complexity can discriminate the threemental states significantly.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
1999年第3期278-282,共5页
Journal of University of Electronic Science and Technology of China
基金
国家教委霍英东青年基金
国家自然科学基金
关键词
脑电图
精神状态
非线性
复杂度
方差分析
electroencephalography
mental state
nonlinear
complexity
analysis of variance
