头皮脑电重测信度的研究进展  

Research Progress of Test-Retest Reliability Based on Scalp EEG

作　　者：覃慧怡 汪玉林 雷旭 Qin Huiyi;Wang Yulin;Lei Xu(Sleep and Neuroimaging Center,Faculty of Psychology,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学心理学部,睡眠神经影像中心,重庆400715

出　　处：《中国生物医学工程学报》2024年第1期106-116,共11页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金(31971028)。

摘　　要：近年来,许多研究应用头皮脑电探讨心理过程的认知神经机制,相关研究高度重视脑电信号的重测信度。基于脑电图开发评估人脑功能的实验方法,是否具有高重测信度备受关注。综述脑电重测信度的主要影响因素,关注脑电基础实验流程对重测信度的影响,为此,首先介绍脑电重测信度的常用测量方法;其次概述该领域实验方法的研究进展,包括实验设计、数据处理、特征选择和被试群体构建等,其中重点阐述脑电数据分析方法对重测信度的影响,揭示提高脑电信号信噪比和建立标准化预处理流程是改善脑电重测信度的重要途径;最后,从实验范式和特征/指标的选择上提出了提高脑电研究重测信度的途径,并展望了脑电重测研究的发展前景。In recent years,the test-retest reliability of EEG has been increasingly emphasized in the exploration of the cognitive neural mechanisms of psychological processes based on scalp electroencephalography(EEG).When developing experimental methods based on scalp EEG to evaluate human brain function,researchers are increasingly concerned about whether these methods are highly reliable.Starting from the main factors that affect the test-retest reliability of EEG,we focused on the impact of the basic experimental process of EEG on the test-retest reliability.Firstly,the commonly used methods for measuring the test-retest reliability of EEG were introduced.Then,from the perspective of experimental methods,the research progress in this field was reviewed,including experimental design,data processing,feature selection,subject group,etc.The main focus was on the impact of data analysis methods on the test-retest reliability.It was pointed out that improving the signal-to-noise ratio of EEG signals and standardizing the preprocessing process are important ways to improve the test-retest reliability of EEG.Finally,ways to improve the test-retest reliability of EEG research were proposed based on the selection of experimental paradigms and features or indicators,and prospects for the development of EEG test-retest research were presented.

关 键 词：脑电 事件相关电位 重测信度 特征 

分 类 号：R318[医药卫生—生物医学工程]