Liew Siaw-Hong, Choo Yun-Huoy, Low Yin Fen, Nor Rashid Fadilla 'Atyka
Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), 94300, Kota Samarahan, Sarawak, Malaysia.
Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), 76100, Durian Tunggal, Melaka, Malaysia.
Brain Inform. 2023 Aug 5;10(1):21. doi: 10.1186/s40708-023-00200-z.
This paper aims to design distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance on the EEG signals. These settings significantly contradict the real-world situations. Thus, making use of the distraction is wiser than eliminating it. The proposed probability-based incremental update strategy is benchmarked with the ground truth (actual class) incremental update strategy. Besides, the proposed technique is also benchmarked with First-In-First-Out (FIFO) incremental update strategy in K-Nearest Neighbour (KNN). The experimental results have shown equivalence discriminatory performance in both high distraction and quiet conditions. This has proven that the proposed distraction descriptor is able to utilize the unique EEG response towards ambient distraction to complement person authentication modelling in uncontrolled environment. The proposed probability-based IncFRNN technique has significantly outperformed the KNN technique for both with and without defining the window size threshold. Nevertheless, its performance is slightly worse than the actual class incremental update strategy since the ground truth represents the gold standard. In overall, this study demonstrated a more practical brainprint authentication model with the proposed distraction descriptor and the probability-based incremental update strategy. However, the EEG distraction descriptor may vary due to intersession variability. Future research may focus on the intersession variability to enhance the robustness of the brainprint authentication model.
本文旨在设计通过对象变化引出的注意力分散描述符，以便使用增量模糊粗糙最近邻（IncFRNN）技术中提出的基于概率的增量更新策略逐步细化粒度知识。大多数脑纹认证模型在受控环境中进行测试，以尽量减少环境干扰对脑电图信号的影响。这些设置与现实世界的情况明显矛盾。因此，利用注意力分散比消除它更明智。所提出的基于概率的增量更新策略以地面真值（实际类别）增量更新策略为基准。此外，所提出的技术还以K近邻（KNN）中的先进先出（FIFO）增量更新策略为基准。实验结果表明，在高注意力分散和安静条件下都具有等效的判别性能。这证明了所提出的注意力分散描述符能够利用对环境注意力分散的独特脑电图响应，以补充在不受控制环境中的人员认证建模。所提出的基于概率的IncFRNN技术在定义窗口大小阈值和未定义窗口大小阈值的情况下均显著优于KNN技术。然而，由于地面真值代表黄金标准，其性能略逊于实际类别增量更新策略。总体而言，本研究通过所提出的注意力分散描述符和基于概率的增量更新策略展示了一个更实用的脑纹认证模型。然而，脑电图注意力分散描述符可能因会话间变异性而有所不同。未来的研究可能会关注会话间变异性，以增强脑纹认证模型的鲁棒性。
