基于微状态的静息态脑电信号分析  

Analysis of Resting State EEG Based on Microstate

作　　者：许学添[1] 蔡跃新[2] XU Xuetian;CAI Yuexin(Department of Information Administration,Guandong Justice Police Vocational College,Guangzhou 510520;Institute of Hearing and Speech-Language Science,Department of Otolaryngology,Sun Yat-sen Memorial Hospital,Sun Yat-sen University,Guangzhou 510120)

机构地区：[1]广东司法警官职业学院信息管理系,广州510520 [2]中山大学孙逸仙纪念医院耳鼻喉科听力学与言语研究所,广州510120

出　　处：《计算机与数字工程》2021年第7期1396-1399,1429,共5页Computer & Digital Engineering

基　　金：国家自然科学基金青年科学基金项目(编号:81600808);广东省普通高校青年创新人才类项目(编号:2018GkQNCX036)资助。

摘　　要：利用微状态的时-空分析特性,采用高密度(128通道)脑电图对20例主观性耳鸣患者和20例对照组的微状态特征进行了研究。通过微状态的平均存在时间、发生频率、覆盖率、振幅和转换率参数的计算与分析,揭示了耳鸣患者和健康人脑电微状态分类的时间特征和语构存在显著差异,也反映了脑电图微状态可以作为大规模脑网络的一种有价值的分析研究方法。Based on the Time-Space analysis characteristic of EEG microstate,the EEG microstate of 20 patients with subjec⁃tive tinnitus and 20 controls are studied by using high density(128 channels)EEG.By calculating and analyzing the parameters of microstate such as average lifespan,occurrence frequency,coverage,amplitude,transition probability,it found that there are sig⁃nificant differences in the temporal characteristics and syntax of the EEG microstate classes at rest between tinnitus patients and the healthy subjects,which also reflects that EEG microstate can be used as a valuable analysis and research method for large-scale brain networks.

关 键 词：微状态 静息态脑电 全脑域功率 耳鸣 聚类分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]