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Towards online applications of EEG biometrics using visual evoked potentials

机译：使用视觉诱发潜力对脑电图生物识别性的在线应用

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摘要

Electroencephalogram (EEG)-based biometrics have attracted increasing attention in recent years. A few studies have used visual evoked potentials (VEPs) in EEG biometrics due to their high signal-to-noise ratio (SNR) and good stability. However, a systematic comparison of different types of VEPs is still lacking. Therefore, this study proposes a system framework for VEP-based biometrics. We quantitatively compared the performance of three types of VEP signals in person identification. Flash VEPs (f-VEPs), steady-state VEPs (ss-VEPs), and codemodulated VEPs (c-VEPs) measured from a group of 21 subjects on two different days were used to estimate the correct recognition rate (CRR). We adopted a template-matching-based identification algorithm that was developed for VEP detection in brain-computer interfaces (BCIs) for person identification. Furthermore, this study demonstrates an online person identification system using c-VEPs with a group of 15 subjects. Among the three methods, c-VEPs achieved the highest CRRs of 100% using 3.15-s VEP data (a 5.25-s duration including 2.1-s intervals) in the intra-session condition and 99.48% using 10.5-s VEP data (a 17.5-s duration including 7-s intervals) in the cross-session condition. The online system achieved a cross-session CRR of 98.93% using 10.5-s VEP data (a 14-s duration including 3.5-s intervals). A systematic comparison of the performance of the three types of VEP signals in EEG-based person identification revealed that the c-VEP paradigm achieved the highest CRRs. The online system further demonstrated high performance in practical applications. The proposed VEPbased biometric system obtained promising identification performance, showing great potential for online person identification applications in real life.
机译：基于脑电图（EEG）的生物识别技术近年来引起了不断的关注。由于其高信噪比（SNR）和良好的稳定性，少数研究使用了EEG生物识别性的视觉诱发电位（VEPS）。然而，仍然缺乏不同类型的VEPS的系统比较。因此，本研究提出了一种基于VEP的生物识别性的系统框架。我们定量比较了三种类型的VEP信号的识别性能。 Flash VEPS（F-VEPS），稳态VEPS（SS-VEPS）和两次不同日子组中的21个受试者测量的CodeModulated Veps（C-VEP）估计了正确的识别率（CRR）。我们采用基于模板匹配的基于模板匹配的识别算法，该算法是为脑 - 计算机接口（BCIS）中的VEP检测而开发的，用于人员识别。此外，本研究演示了使用具有一组15个科目的C-VEPS的在线人识别系统。在这三种方法中，C-VEPS使用3.15-S VEP数据（包括2.1-S间隔的5.25-S持续时间）在10.5-S VEP数据中获得了100％的最高CRR（5.25-S间隔）（a 17.5-S持续时间，包括7-S间隔）在交叉会话条件下。在线系统使用10.5-S VEP数据（包括3.5-S间隔的14秒）实现了98.93％的跨会会话CRR。在基于EEG的人身份识别中的三种类型VEP信号的性能的系统比较显示C-VEP范例达到了最高的CRR。在线系统进一步在实际应用中表现出高性能。提出的VEPAMASED生物识别系统获得了有希望的识别性能，显示出在现实生活中的在线人识别应用的巨大潜力。

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  • 来源
    《Expert systems with applications》 |2021年第9期|114961.1-114961.9|共9页
  • 作者

    Zhao Hongze; Chen Yuanfang; Pei Weihua; Chen Hongda; Wang Yijun;

  • 作者单位

    Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China;

    Beijing Machine & Equipment Inst Beijing 100854 Peoples R China;

    Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Biometrics; Electroencephalography; Person identification; Visual evoked potentials; Pattern analysis;

    机译：生物识别;脑电图;人识别;视觉诱发潜力;模式分析;

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