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On the channel density of EEG signals for reliable biometric recognition
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.patrec.2021.04.003
Min Wang , Kathryn Kasmarik , Anastasios Bezerianos , Kay Chen Tan , Hussein Abbass

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.



中文翻译:

脑电信号的通道密度可实现可靠的生物识别

脑电图(EEG)通过包含独特的属性(包括针对伪造的鲁棒性,隐私遵从性和活跃性检测)提供了吸引人的生物统计信息。在实际应用中部署EEG生物识别系统的主要挑战中,稳定性和可用性是两个重要挑战。它们分别反映了系统在不同状态之内和之间提供稳定性能的能力,以及系统的易用性。先前的研究表明,EEG生物识别系统的可用性在很大程度上受到电极数量的影响,降低通道密度是提高可用性的有效方法。但是,仍不清楚通道密度对识别性能和稳定性的影响。本研究针对使用不同特征提取和分类方法的系统检查了此问题。我们的结果揭示了通道密度和稳定性之间的权衡。使用低密度脑电图会在不同程度上损害识别准确性和稳定性。在分析的基础上,我们提出了一个框架,该框架整合了通道密度增强,功能连通性估计和深度学习模型,用于实用且稳定的EEG生物特征识别系统。该框架有助于提高使用消费级低通道密度设备的EEG生物识别系统的稳定性,同时保留高可用性的优点。我们提出了一个框架,该框架整合了通道密度增强,功能连通性估计和深度学习模型,用于实用且稳定的EEG生物识别系统。该框架有助于提高使用消费级低通道密度设备的EEG生物识别系统的稳定性,同时保留高可用性的优点。我们提出了一个框架,该框架整合了通道密度增强,功能连通性估计和深度学习模型,用于实用且稳定的EEG生物识别系统。该框架有助于提高使用消费级低通道密度设备的EEG生物识别系统的稳定性,同时保留高可用性的优点。

更新日期:2021-05-04
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