Wang Haili, Yin Ning, Xu Guizhi
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China.
Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Feb 25;40(1):163-170. doi: 10.7507/1001-5515.202206007.
Electroencephalogram EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.
脑电图（EEG）具有高时间分辨率的特点，近年来各种脑电图分析方法发展迅速。脑电图微状态分析方法可用于研究大脑在毫秒尺度上的变化，还能在拓扑层面呈现脑电图信号的分布，从而反映全脑的不连续和非线性特征。经过30多年的丰富和完善，脑电图微状态分析已渗透到许多与脑科学相关的研究领域。本文总结了脑电图微状态分析方法的基本原理，系统描述了微状态特征参数的变化、微状态与脑功能网络的关系以及微状态特征提取和分类在脑部疾病和脑认知应用中的主要进展，希望为该领域的研究人员提供一些参考。
