The increasing adoption of Extended Reality (XR) in various applications underscores the need for secure and user-friendly authentication methods. However, existing methods can disrupt the immersive experience in XR settings, or suffer from higher false acceptance rates. In this paper, we introduce a multimodal biometric authentication system that combines eye movement and brainwave patterns, as captured by consumer-grade low-fidelity sensors. Our multimodal authentication exploits the non-invasive and hands-free properties of eye movement and brainwaves to provide a seamless XR user experience and enhanced security as well. Using synchronized eye and brainwave data collected from 30 participants through consumer-grade devices, we investigated whether twin neural networks can utilize these biometrics for identity verification. Our multimodal authentication system yields an excellent Equal Error Rate (EER) of 0.298\%, which means an 83.6\% reduction in EER compared to the single eye movement modality or a 93.9\% reduction in EER compared to the single brainwave modality.

翻译：随着扩展现实（XR）在各领域的广泛应用，亟需兼具安全性和用户友好性的认证方法。然而，现有方法可能破坏XR环境的沉浸式体验，或面临较高的误接受率。本文提出一种结合眼动与脑电波模式的多模态生物特征认证系统，通过消费级低精度传感器采集数据。该系统利用眼动与脑电信号的非侵入性与免手持特性，在保障XR用户无缝体验的同时提升安全性。我们通过消费级设备采集30名受试者的同步眼动与脑电数据，探究孪生神经网络能否利用这些生物特征实现身份验证。实验表明，本多模态认证系统取得了0.298%的优异等错误率（EER），相较于单一眼动模态和单一脑电模态，EER分别降低了83.6%和93.9%。