基于脑电信号的身份特征提取方法研究综述  

Review on identity feature extraction methods based on electroencephalogram signals

作　　者：钟文潇 安兴伟 狄洋 张力新(综述) 明东(审校)[1,2] ZHONG Wenxiao;AN Xingwei;DI Yang;ZHANG Lixin;MING Dong(Department of Biomedical Engineering,College of Precision Instruments and Optoelectronics Engineering,Tianjin University,TianJin 300072,P.R.China;Department of Biomedical Engineering,Academy of Medical Engineering and Translational Medicine,TianJin University,TianJin 300072,P.R.China)

机构地区：[1]天津大学医学工程与转化医学研究院,天津300072 [2]天津大学精密仪器与光电子工程学院生物医学工程系,天津300072

出　　处：《生物医学工程学杂志》2021年第6期1203-1210,共8页Journal of Biomedical Engineering

基　　金：国家重点研发计划项目(2017YFB1300302);国家自然科学基金项目(61603269,81630051)。

摘　　要：生物识别技术在当今信息社会中发挥着重要作用。脑电信号(EEG)作为一种新型的生物特征,在通用性、持久性和安全性等方面具有独特优势,基于脑电信号个体差异性的身份识别研究目前开始受到广泛的关注。身份特征提取是实现良好识别性能的重要步骤,如何结合脑电数据的特点,更好地提取脑电信号中的差异性信息,是近年来基于脑电信号的身份识别领域的研究热点。本文综述了基于脑电信号常用的身份特征提取方法,包括单导联特征、导联间特征、深度学习方法以及基于空间滤波的特征提取方法等,并阐述各种特征提取方法的基本原理、应用方式及相关成果。最后归纳目前存在的问题并对发展趋势进行展望。Biometrics plays an important role in information society. As a new type of biometrics,electroencephalogram(EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals,including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.

关 键 词：脑电 身份识别 特征提取 单导联特征 导联间特征 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]