Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2021 Mar 17;21(6):2097. doi: 10.3390/s21062097.
In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.
近年来，脑电图（EEG）信号已被用作一种生物识别模式，基于 EEG 的生物识别系统受到了越来越多的关注。然而，由于 EEG 信号的敏感性，通过处理技术提取身份信息可能会导致提取的身份信息丢失一些。这可能会影响系统中主体之间的独特性。在这种情况下，我们提出了一种新的基于 EEG 的生物识别系统的自我相对评估框架。该框架旨在当生物识别系统向新的被试注册时，选择更准确的身份信息。实验是在公开的 EEG 数据集上进行的，这些数据集是从 108 名闭眼休息状态的受试者中收集的。结果表明，开放条件有助于选择更准确的身份信息。
