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脑电信号预处理方法研究综述
引用本文:骆睿鹏,冯铭科,黄鑫,邹任玲,李丹.脑电信号预处理方法研究综述[J].电子科技,2023,36(4):36-43.
作者姓名:骆睿鹏  冯铭科  黄鑫  邹任玲  李丹
作者单位:上海理工大学 健康科学与工程学院,上海 200093
基金项目:国家自然科学基金(61803265);国家重点研发计划(2018YFB1307200);上海市科技创新行动计划产学研医合作领域项目(21S31906000)
摘    要:脑电信号是一种复杂且重要的生物信号,被广泛应用于类脑智能技术和脑机接口领域的研究。文中介绍了干扰正常脑电信号的常见非生理性伪迹和生理性伪迹的类型及特点,并对生理性伪迹的产生原因进行了详细分析。通过对各种脑电信号去除伪迹方法的回顾以及应用现状的分析,比较并总结了传统去除伪迹方法和新型去除伪迹方法的研究进展,并进一步分析去除伪迹方法的优缺点。部分方法已经成功应用于处理脑电信号中的眼电、心电和肌电等伪迹中。文中还针对目前脑电信号去除伪迹的需求及所面临的问题给出了应对策略,并对未来的研究方向进行了分析和展望。

关 键 词:脑电信号  预处理  去伪迹  盲源分离  小波变换  EMD  人工神经网络  深度学习  
收稿时间:2021-10-11

A Review of Research on EEG Signal Preprocessing Methods
LUO Ruipeng,FENG Mingke,HUANG Xin,ZOU Renling,LI Dan.A Review of Research on EEG Signal Preprocessing Methods[J].Electronic Science and Technology,2023,36(4):36-43.
Authors:LUO Ruipeng  FENG Mingke  HUANG Xin  ZOU Renling  LI Dan
Institution:School of Health Science and Engineering,University of Shanghai for Science and Technology, Shanghai 200093,China
Abstract:EEG signal is a complex and important biological signal, which is widely used in the research of brain-like intelligence technology and brain-computer interface. In this study, the types and characteristics of common non-physiological artifacts and physiological artifacts that interfere with normal EEG signals are introduced, and the causes of physiological artifacts are analyzed in detail. Through the review of various EEG artifact removal methods and the analysis of the application status, the research progress of traditional artifact removal methods and new artifact removal methods is compared and summarized, and the advantages and disadvantages of artifact removal methods are further analyzed. Some methods have been successfully applied to the processing of electrocardiogram, ECG and EMG artifacts in EEG signals.The current demand for artifact removal from EEG signals and the problems faced are also given in the present study, and the future research directions are analyzed and prospected.
Keywords:EEG signal  preprocessing  artifacts removal  BSS  wavelet transform  EMD  artificial neural network  deep learning  
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