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：基于脑电活动的生物识别系统已被提出作为密码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、免手动、可访问甚至可撤销的认证解决方案。然而，尽管该领域研究持续增长，可重复性问题阻碍了其快速发展。例如，缺乏性能结果和系统配置的标准报告方案、缺少通用评估基准等问题，使得不同生物识别解决方案的可比性与合理评估变得困难。此外，当研究代码通常不以开放获取方式公开时，会对后续工作造成障碍。为弥补这一空白，我们提出NeuroIDBench——一个用于基准测试脑电波身份认证模型的灵活开源工具。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和机器学习算法集，支持在两种常见攻击者模型（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性能报告与可视化结果。我们利用NeuroIDBench检验了文献中提出的浅层分类器与基于深度学习的方法，并测试了跨会话的鲁棒性。研究发现，在未知攻击者场景（通常未在文献中测试）中等错误率（EER）降低37.6%，同时揭示了会话变异性对脑电波认证的重要性。总之，我们的结果证明了NeuroIDBench在简化算法公平比较方面的可行性与重要性，从而通过稳健的方法学实践推动脑电波身份认证的进步。