计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 1224-1228.DOI: 10.11772/j.issn.1001-9081.2018092037

• 应用前沿、交叉与综合 • 上一篇    下一篇

以脑电图子序列特征向量为节点的脑网络分析方法

杨雄, 姚蓉, 杨鹏飞, 王哲, 李海芳   

  1. 太原理工大学 信息与计算机学院, 太原 030024
  • 收稿日期:2018-10-09 修回日期:2018-11-27 出版日期:2019-04-10 发布日期:2019-04-10
  • 通讯作者: 李海芳
  • 作者简介:杨雄(1993-),男,山西长治人,硕士研究生,CCF会员,主要研究方向:医学信号处理与分析;姚蓉(1994-),女,山西运城人,硕士研究生,主要研究方向:医学信号处理与分析;杨鹏飞(1994-),男,河南南阳人,硕士研究生,主要研究方向:医学信号处理与分析;王哲(1994-),女,山西大同人,硕士研究生,主要研究方向:图像处理;李海芳(1963-),女,山西晋中人,教授,博士,CCF高级会员,主要研究方向:脑信息学、大数据处理、计算机视觉。
  • 基金资助:
    国家自然科学基金资助项目(1472270)。

Brain network analysis method based on feature vector of electroencephalograph subsequence

YANG Xiong, YAO Rong, YANG Pengfei, WANG Zhe, LI Haifang   

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan Shanxi 030024, China
  • Received:2018-10-09 Revised:2018-11-27 Online:2019-04-10 Published:2019-04-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (1472270).

摘要: 工作记忆复杂网络分析方法大多数是以通道作为节点从空间的角度进行分析,很少有从时间角度对通道网络进行分析。针对脑电图(EEG)的高时间分辨率特性及时间序列分段较难的缺陷,提出一种从时间角度构建网络并对网络进行分析的方法。首先,利用微状态将每个通道的EEG信号划分成不同的子段作为网络的节点;其次,在子段中提取并选择有效特征作为子段的特征,计算子段特征向量之间的相关性构建通道时间序列复杂网络;最后,对所构建网络的属性及相似性进行分析,并在精神分裂症患者EEG数据上进行验证。实验结果表明,通过所提方法对精神分裂症数据进行分析,能够充分利用EEG信号的时间特性从时间角度深入了解精神分裂症病人工作记忆中构建的时间序列通道网络的特点,解释了精神分裂症患者与正常人的显著性差异。

关键词: 脑电图, 复杂网络, 工作记忆, 精神分裂症, 微状态

Abstract: Working memory complex network analysis methods mostly use channels as nodes to analyze from the perspective of space, while rarely analyze channel networks from the perspective of time. Focused on the high time resolution characteristics of ElectroEncephaloGraph (EEG) and the difficulty of time series segmentation, a method of constructing and analyzing network from the time perspective was proposed. Firstly, the microstate was used to divide EEG signal of each channel into different sub-segments as nodes of the network. Secondly, the effective features in the sub-segments were extracted and selected as the sub-segment effective features, and the correlation between sub-segment feature vectors was calculated to construct channel time sequence complex network. Finally, the attributes and similarity analysis of the constructed network were analyzed and verified on the schizophrenic EEG data. The experimental results show that the analysis of schizophrenia data by the proposed method can make full use of the time characteristics of EEG signals to understand the characteristics of time series channel network constructed in working memory of patients with schizophrenia from a time perspective, and explain the significant differences between patients and normals.

Key words: ElectroEncephaloGraph (EEG), complex network, working memory, schizophrenia, microstate

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