Chan Hui-Ling, Kuo Po-Chih, Cheng Chia-Yi, Chen Yong-Sheng
Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.
Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, Taiwan.
Front Neuroinform. 2018 Oct 9;12:66. doi: 10.3389/fninf.2018.00066. eCollection 2018.
The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.
数字世界的出现极大地增加了用户必须记住的账户和密码数量。这也增加了对云环境中个人信息进行安全访问的需求。生物识别技术是一种身份识别方法，可用于识别和认证。在已开发的各种模式中，基于脑电图（EEG）的生物识别技术具有无与伦比的通用性、独特性和可采集性，同时将被规避的风险降至最低。然而，将基于EEG的身份识别商业化面临诸多挑战。本文回顾了过去几年提出的各种系统，重点关注阻碍大规模实施的缺点，包括与时间稳定性、心理和生理变化、协议设计、设备以及性能评估相关的问题。我们还研究了基于EEG的可用识别系统进一步发展的几个方向以及它们可应用的利基市场。预计EEG仪器、设备上处理和机器学习技术的快速进步将在不久的将来催生商业化的身份识别系统。
