NeuroIDBench：面向脑电波认证研究方法学标准化的开源基准框架
NeuroIDBench: An Open-Source Benchmark Framework for the Standardization of Methodology in Brainwave-based Authentication Research
AI总结：
针对脑电波认证研究存在的可复现性差、缺乏统一评估基准等问题，提出开源基准框架NeuroIDBench，整合多数据集与算法，支持两种对抗场景测试，验证了其在算法公平比较与推动领域发展中的价值。
摘要：
基于大脑活动的生物识别系统已被提出作为密码的替代方案，或用于补充现有认证技术。这类系统利用个体独特的脑电波模式，有望打造出防窃取、免动手、易获取，甚至具备可撤销性的认证解决方案。然而，尽管该领域的研究成果不断涌现，可复现性问题却阻碍了研究的更快推进。诸如性能结果与系统配置缺乏标准报告方案、缺少通用评估基准等问题，使得不同生物识别方案的可比性与合理评估颇具挑战。此外，源代码通常不公开开放获取，这也给后续研究设置了障碍。为填补这一空白，我们推出了NeuroIDBench——一款用于基准测试脑电波认证模型的灵活开源工具。它整合了9个多样化的数据集，实现了一整套预处理参数与机器学习算法，支持在两种常见对抗模型（已知攻击者与未知攻击者）下开展测试，还能让研究人员生成完整的性能报告与可视化结果。我们借助NeuroIDBench，对文献中提出的浅层分类器与基于深度学习的方法展开研究，并测试了模型在多会话场景下的鲁棒性。我们观察到，在未知攻击者场景（文献中通常未开展测试）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认证的重要性。总体而言，我们的研究结果证明了NeuroIDBench在简化算法公平比较方面的可行性与实用价值，能够通过稳健的方法学实践推动脑电波认证研究的发展。
英文摘要：
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.
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