基于脑电信号的疲劳驾驶检测研究综述  

A review of EEG signal-based fatigue driving detection

作　　者：王忠民 郑镕林[1] 赵玉鹏 贺炎 WANG Zhongmin;ZHENG Ronglin;ZHAO Yupeng;HE Yan(School of Computer Science and Technology,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;Xi’an Key Laboratory of Big Data and Intelligent Computing,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学计算机学院,陕西西安710121 [2]西安邮电大学、西安市大数据与智能计算重点实验室,陕西西安710121 [3]西安邮电大学、陕西省网络数据分析与智能处理重点实验室,陕西西安710121

出　　处：《西安邮电大学学报》2022年第6期52-66,共15页Journal of Xi’an University of Posts and Telecommunications

基　　金：国家自然科学基金项目(61373116);陕西省教育厅科学计划研究项目(18JK0697)。

摘　　要：通过检索脑电信号进行疲劳驾驶检测的相关文献,归纳了现有的研究成果,整理了当下主流的研究方法。分别介绍了脑电信号采集、预处理、特征提取和分类识别方法,综合比较了脑电信号分别在时域、频域和脑功能网络等特征提取方法的优势及局限性,说明了脑电信号的各种特征属性以及脑电信号的空间特性,阐述了信号分析方法的适用场景,最后讨论了该领域未来的发展方向。Through the study of related literatures on fatigue driving detection by EEG signals,the current research results and main research methods are summarized.The methods of EEG signal acquisition,pre-processing,feature extraction,and classification recognition are introduced respectively.The advantages and disadvantages of EEG signal feature extraction methods in the time domain,frequency domain,and brain function network are compared comprehensively.The various feature attributes of EEG signal and the spatial characteristics of EEG signal are explained.The application scenarios of signal analysis methods are elaborated.The future development direction of this field is discussed.

关 键 词：脑电信号 疲劳驾驶 特征提取 脑网络 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]