计算机工程与应用 ›› 2019, Vol. 55 ›› Issue (5): 60-64.DOI: 10.3778/j.issn.1002-8331.1803-0455

• 理论与研发 • 上一篇    下一篇

精分EEG脑网络同步稳定性研究

姚  蓉1,杨  雄1,杨鹏飞1,阴桂梅1,2,李海芳1   

  1. 1.太原理工大学 信息与计算机学院,太原 030600
    2.太原师范学院 计算机系,山西 晋中 030619
  • 出版日期:2019-03-01 发布日期:2019-03-06

Study on Synchronization Stability of Compelx Brain Network:Schizophrenia EEG

YAO Rong1, YANG Xiong1, YANG Pengfei1, YIN Guimei1,2, LI Haifang1   

  1. 1.College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
    2.Department of Compute Science, Taiyuan Normal University, Jinzhong, Shanxi 030619, China
  • Online:2019-03-01 Published:2019-03-06

摘要: 为了深入表征和刻画精神分裂症患者大脑活动时各个电极通道的状态变化,通过利用复杂网络同步稳定理论以及精分工作记忆实验范式对EEG信号进行分析。从复杂网络角度出发构建脑功能网络,并利用特征谱比值法分析脑网络及其同步性随时间的演化过程。对比实验表明精分患者和正常对照组同步能力具有很大差异且差异主要源于对应脑网络的一个局部化区域S的不同,并通过设计对比实验进一步验证此区域对脑网络同步影响的有效性。脑网络同步稳定区域S的发现对研究神经精神性疾病下脑网络的演化过程提供了新的思路。

关键词: 脑电图, 精神分裂症, 工作记忆, 复杂网络, 脑网络同步

Abstract: In order to further characterize changes in the state of each electrode channel during brain activity in schizophrenic patients, the article uses complex network synchronization stability theory and the working memory SMST experimental paradigm to analyze the EEG signals. The brain function network is constructed from the perspective of complex networks and the evolution of the brain network and its synchronization over time is analyzed using the characteristic spectrum ratio method. Comparing the simulation experiments, it shows that the synchronization ability of the refined patients and the normal control group is very different and the difference is mainly due to the difference in a localized region S of the corresponding brain network. The comparative experiments are designed to further verify the effect of this region on the synchronization of brain networks. The discovery of the brain-synchronous and stable region S has provided new ideas for studying the evolution of brain networks under neuropsychiatric diseases.

Key words: Electroencephalogram(EEG), schizophrenia, working memory, complex network, brain network synchronization