Mu Zhendong, Yin Jinhai, Hu Jianfeng
Center of Collaboration and Innovation, Jiangxi University of Technology, Nanchang, Jiangxi Province, 330098, P.R. China.
J Integr Neurosci. 2018 Aug 15;17(1):53-60. doi: 10.31083/JIN-170042.
In this paper, a personal authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. To achieve performance stability, a sequence of self-face photographs including first-occurrence position and non-first-occurrence position are taken into account in the serial occurrence of visual stimuli. Additionally, a Fisher linear classification method and event-related potential technique for feature analysis is adapted to yield remarkably better outcomes than those obtained by most existing
本文提出了一种个人认证系统，该系统可以通过响应自拍和非自拍照片生成独特的脑电图信号特征来有效识别个体。为了实现性能稳定性，在视觉刺激的连续出现中考虑了一系列包括首次出现位置和非首次出现位置的自拍照片。此外，采用了一种用于特征分析的Fisher线性分类方法和事件相关电位技术，以产生比大多数现有方法明显更好的结果。
