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.

中文翻译：

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使用基于概率的增量模糊粗糙最近邻的脑纹认证建模的分心描述符

本文旨在设计干扰描述符，通过对象变化引出，以增量地细化粒度知识，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。大多数脑纹认证模型都是在良好控制的环境中进行测试，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，利用干扰比消除干扰更明智。所提出的基于概率的增量更新策略以地面实况（实际类）增量更新策略为基准。此外，所提出的技术还以 K 最近邻（KNN）中的先进先出（FIFO）增量更新策略为基准。实验结果表明，在高度分散注意力和安静的条件下，其辨别性能是等效的。这证明了所提出的分心描述符能够利用对环境分心的独特脑电图响应来补充不受控环境中的人员身份验证建模。无论是否定义窗口大小阈值，所提出的基于概率的 IncFRNN 技术都明显优于 KNN 技术。然而，它的性能比实际的类增量更新策略稍差，因为地面事实代表了黄金标准。总的来说，本研究展示了一种更实用的脑纹认证模型，其中包括所提出的干扰描述符和基于概率的增量更新策略。然而，脑电图分心描述符可能会因会话期间的变异性而变化。未来的研究可能集中在会话间的变异性上，以增强脑纹认证模型的稳健性。