China National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Biomed Eng Online. 2018 May 3;17(1):55. doi: 10.1186/s12938-018-0483-7.
The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, and non-coercion. In order to enhance its applicability in identity authentication, a novel EEG-based identity authentication method is proposed based on self- or non-self-face rapid serial visual presentation.
In contrast to previous studies that extracted EEG features from rest state or motor imagery, the designed paradigm could obtain a distinct and stable biometric trait with a lower time cost. Channel selection was applied to select specific channels for each user to enhance system portability and improve discriminability between users and imposters. Two different imposter scenarios were designed to test system security, which demonstrate the capability of anti-deception. Fifteen users and thirty imposters participated in the experiment. The mean authentication accuracy values for the two scenarios were 91.31 and 91.61%, with 6 s time cost, which illustrated the precision and real-time capability of the system. Furthermore, in order to estimate the repeatability and stability of our paradigm, another data acquisition session is conducted for each user. Using the classification models generated from the previous sessions, a mean false rejected rate of 7.27% has been achieved, which demonstrates the robustness of our paradigm.
Experimental results reveal that the proposed paradigm and methods are effective for EEG-based identity authentication.
脑电图（EEG）信号代表了主体特定的大脑活动模式，由于其具有卓越的不可见性、非克隆性和非强制性，因此被认为是一种理想的生物识别特征。为了提高其在身份验证中的适用性，提出了一种基于自我或非自我面孔快速序列视觉呈现的新型 EEG 身份验证方法。
与之前从静息状态或运动想象中提取 EEG 特征的研究不同，所设计的范式可以以较低的时间成本获得独特而稳定的生物特征。通道选择被应用于为每个用户选择特定的通道，以增强系统的便携性并提高用户与赝品之间的可区分性。设计了两种不同的赝品场景来测试系统的安全性，这证明了该系统具有反欺骗能力。十五名用户和三十名赝品参与了实验。两个场景的平均认证准确率分别为 91.31%和 91.61%，时间成本为 6 秒，这说明了系统的精度和实时性。此外，为了估计我们范式的可重复性和稳定性，为每个用户进行了另一次数据采集会话。使用之前会话生成的分类模型，平均错误拒绝率为 7.27%，这证明了我们范式的稳健性。
实验结果表明，所提出的范式和方法对于基于 EEG 的身份验证是有效的。
