Zhong Wenxiao, An Xingwei, Di Yang, Zhang Lixin, Ming Dong
Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China.
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, TianJin 300072, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Dec 25;38(6):1203-1210. doi: 10.7507/1001-5515.202102057.
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.
生物识别技术在信息社会中发挥着重要作用。作为一种新型生物识别技术，脑电图（EEG）信号在通用性、耐久性和安全性方面具有特殊优势。目前，基于EEG信号的个体识别方法研究备受关注。身份特征提取是实现良好识别性能的重要一步。如何结合EEG数据的特点，更好地提取EEG信号中的差异信息，是近年来基于EEG的身份识别领域的研究热点。本文综述了基于EEG信号的常用身份特征提取方法，包括单通道特征、通道间特征、深度学习方法和基于空间滤波器的特征提取方法等，并阐述了各种特征提取方法的基本原理、应用方法和相关成果。最后，我们总结了当前存在的问题并预测了发展趋势。
