Biometric systems based on brain activity have been proposed as an alternative to passwords or to complement current authentication techniques. By leveraging the unique brainwave patterns of individuals, these systems offer the possibility of creating authentication solutions that are resistant to theft, hands-free, accessible, and potentially even revocable. However, despite the growing stream of research in this area, faster advance is hindered by reproducibility problems. Issues such as the lack of standard reporting schemes for performance results and system configuration, or the absence of common evaluation benchmarks, make comparability and proper assessment of different biometric solutions challenging. Further, barriers are erected to future work when, as so often, source code is not published open access. To bridge this gap, we introduce NeuroIDBench, a flexible open source tool to benchmark brainwave-based authentication models. It incorporates nine diverse datasets, implements a comprehensive set of pre-processing parameters and machine learning algorithms, enables testing under two common adversary models (known vs unknown attacker), and allows researchers to generate full performance reports and visualizations. We use NeuroIDBench to investigate the shallow classifiers and deep learning-based approaches proposed in the literature, and to test robustness across multiple sessions. We observe a 37.6% reduction in Equal Error Rate (EER) for unknown attacker scenarios (typically not tested in the literature), and we highlight the importance of session variability to brainwave authentication. All in all, our results demonstrate the viability and relevance of NeuroIDBench in streamlining fair comparisons of algorithms, thereby furthering the advancement of brainwave-based authentication through robust methodological practices.

翻译：基于脑活动的生物特征识别系统已被提出作为密码的替代方案或现有认证技术的补充。通过利用个体独特的脑电波模式，这些系统有望创建抗窃取、免手持、可访问且甚至可能可撤销的身份认证解决方案。然而，尽管该领域研究持续增长，可复现性问题阻碍了更快的进展。例如性能结果与系统配置缺乏标准化报告方案、缺少通用评估基准等问题，使得不同生物特征解决方案的可比性与合理评估面临挑战。此外，当源代码未开放获取时（这种情况屡见不鲜），也为后续研究设置了障碍。为弥合这一鸿沟，我们推出了NeuroIDBench——一个用于脑电波身份认证模型基准测试的灵活开源工具。该工具整合了九个多样化数据集，实现了全面的预处理参数与机器学习算法集，支持在两种常见对抗模型（已知攻击者与未知攻击者）下进行测试，并允许研究人员生成完整的性能报告与可视化结果。我们运用NeuroIDBench对文献中提出的浅层分类器与基于深度学习的方法进行研究，并测试跨多会话场景的鲁棒性。我们观察到在未知攻击者场景（文献中通常未测试）下等错误率（EER）降低了37.6%，同时强调了会话变异性对脑电波身份认证的重要性。总体而言，我们的研究结果证明了NeuroIDBench在促进算法公平比较方面的可行性与相关性，从而通过稳健的方法学实践推动脑电波身份认证领域的发展。