静息态脑电图的微状态和欧米伽复杂性分析
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography.
作者信息
Gao Fei, Jia Huibin, Feng Yi
机构信息
Department of Pain Medicine, Peking University People's Hospital.
Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, School of Biological Sciences & Medical Engineering, Southeast University;
出版信息
J Vis Exp. 2018 Jun 15(136):56452. doi: 10.3791/56452.
Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.
微状态和ω复杂性是两种无需参考的脑电图（EEG）测量方法，它们可以表征EEG数据的时间和空间复杂性，并已被广泛用于研究某些脑部疾病的神经机制。本文的目的是逐步描述EEG微状态和ω复杂性分析背后的方案。这两种测量方法的主要优点是它们可以消除传统频谱分析中固有的参考依赖问题。此外，微状态分析充分利用了静息态EEG的高时间分辨率，获得的四个微状态类别可以分别与相应的静息态网络相匹配。ω复杂性表征了整个大脑或特定脑区的空间复杂性，与专注于单通道信号复杂性的传统复杂性测量方法相比具有明显优势。这两种EEG测量方法可以相互补充，分别从时间和空间域研究大脑复杂性。
Zotero 插件