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1 |
On the channel density of EEG signals for reliable biometric recognition
چگالی کانال سیگنال های EEG برای تشخیص بیومتریک قابل اعتماد-2021 Electroencephalography (EEG) provides appealing biometrics by encompassing unique attributes including robustness against forgery, privacy compliance, and aliveness detection. Among the main challenges
in deploying EEG biometric systems in real-world applications, stability and usability are two important
ones. They respectively reflect the capacity of the system to provide stable performance within and across
different states, and the ease of use of the system. Previous studies indicate that the usability of an EEG
biometric system is largely affected by the number of electrodes and reducing channel density is an effective way to enhance usability. However, it is still unclear what is the impact of channel density on
recognition performance and stability. This study examines this issue for systems using different feature
extraction and classification methods. Our results reveal a trade-off between channel density and stability. With low-density EEG, the recognition accuracy and stability are compromised to varying degrees.
Based on the analysis, we propose a framework that integrates channel density augmentation, functional
connectivity estimation and deep learning models for practical and stable EEG biometric systems. The
framework helps to improve the stability of EEG biometric systems that use consumer-grade low channel
density devices, while retaining the advantages of high usability. Keywords: EEG biometrics | Data augmentation | Deep learning | Current source density |
مقاله انگلیسی |
2 |
Towards online applications of EEG biometrics using visual evoked potentials
در جهت کاربردهای آنلاین بیومتریک EEG با استفاده از پتانسیل های برانگیخته بصری-2021 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 code- modulated 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 VEP- based biometric system obtained promising identification performance, showing great potential for online per- son identification applications in real life. Keywords: Biometrics | Electroencephalography | Person identification | Visual evoked potentials | Pattern analysis |
مقاله انگلیسی |