基于复杂网络的脑电信号回归分析  

Complex-network-based approach to recurrence analysis of EEG

作　　者：蔡世民[1] 洪磊[1] 傅忠谦[1] 周佩玲[1] 

机构地区：[1]中国科学技术大学电子科学与技术系,安徽合肥230027

出　　处：《中国科学技术大学学报》2011年第4期330-333,340,共5页JUSTC

基　　金：国家自然科学基金(60874090;60974079;61004102);教育部博士后基金(20100480704);王宽诚博士后基金;中央高校基本科研业务费专项基金资助

摘　　要：基于相点距离集合确定脑电信号的相空间重构嵌入维数,并得到时间序列的相空间轨迹.把由相空间轨迹构成的回归矩阵转化为复杂网络的连接矩阵,并用复杂网络的特征参量表征回归矩阵的性质.实验结果表明,由不同生理状态的脑电信号构成的回归矩阵呈现出普适和非普适特征,也就是小世界特征的一致性和聚类结构的差异性.这些结论与实际脑网络的小世界特征和脑功能区域的聚类结构能够很好吻合.The phase-space trajectories of EEG signals were obtained via determining embedding dimension in phase-space reconstruction based on the distance set of space points.The recurrence matrix calculated from phase-space trajectories was identified with the adjacency matrix of a complex network and measured by the characterisation of complex networks.The results show that the small-world characteristic of recurrence matrix exists in all physiological states,while the cluster structure of that changes with different physiological states.The universal and non-universal properties of recurrence matrix show good consistency with these of the empirical brain networks.

关 键 词：复杂网络 脑电信号 回归分析 小世界 

分 类 号：R318[医药卫生—生物医学工程]