The field of brainwave-based biometrics has gained attention for its potential to revolutionize user authentication through hands-free interaction, resistance to shoulder surfing, continuous authentication, and revocability. However, current research often relies on single-session or limited-session datasets with fewer than 55 subjects, raising concerns about the generalizability of the findings. To address this gap, we conducted a large-scale study using a public brainwave dataset comprising 345 subjects and over 6,007 sessions (an average of 17 per subject) recorded over five years using three headsets. Our results reveal that deep learning approaches significantly outperform hand-crafted feature extraction methods. We also observe Equal Error Rates (EER) increases over time (e.g., from 6.7% after 1 day to 14.3% after a year). Therefore, it is necessary to reinforce the enrollment set after successful login attempts. Moreover, we demonstrate that fewer brainwave measurement sensors can be used, with an acceptable increase in EER, which is necessary for transitioning from medical-grade to affordable consumer-grade devices. Finally, we compared our results to prior work and existing biometric standards. While our performance is on par with or exceeds previous approaches, it still falls short of industrial benchmarks. Based on the results, we hypothesize that further improvements are possible with larger training sets. To support future research, we have open-sourced our analysis code.

翻译：脑电波生物识别领域因其通过免手交互、抗肩窥攻击、持续认证和可撤销性等潜力革新用户认证而备受关注。然而，当前研究通常依赖于单会话或有限会话数据集，且受试者数量少于55人，这引发了研究结果普适性的担忧。为弥补这一空白，我们利用一个公开脑电波数据集开展了一项大规模研究，该数据集包含345名受试者、超过6,007个会话（平均每人17个），使用三种头戴设备在五年内记录完成。我们的结果表明，深度学习方法显著优于手工特征提取方法。我们还观察到等错误率（EER）随时间推移而上升（例如，从1天后的6.7%增至一年后的14.3%）。因此，有必要在成功登录尝试后强化注册集。此外，我们证明了可以使用更少的脑电波测量传感器，同时EER的增加在可接受范围内，这对于从医疗级设备过渡到经济实惠的消费级设备是必要的。最后，我们将结果与先前工作和现有生物识别标准进行了比较。虽然我们的性能与先前方法相当或更优，但仍未达到工业基准。基于这些结果，我们假设使用更大的训练集可能实现进一步改进。为支持未来研究，我们已开源分析代码。