Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

Khanh Ha Nguyen, Yvonne Tran, Ashley Craig, Hung Nguyen, Rifai Chai
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Abstract

Objective.While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.Approach.In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.

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使用脑电图源空间功能连接特征进行聚类,以减少驾驶疲劳分类中的受试者变异性。
虽然基于脑电图(EEG)的驾驶员疲劳状态分类模型已证明有效,但其在现实世界中的应用仍不确定。不同个体之间的脑电信号存在很大差异,这对开发通用模型构成了挑战,往往需要在引入新受试者后进行重新训练。然而,在现实世界中,获取足够的数据进行再训练,尤其是新受试者的疲劳数据是不切实际的。为了应对这些挑战,本文介绍了一种将聚类与分类相结合的疲劳检测混合解决方案。无监督聚类根据受试者在警戒状态下的脑电图功能连接性对其进行分组,随后将分类模型应用于每个聚类,以预测警戒和疲劳状态。结果表明,与没有聚类的情况相比,聚类分类的准确率更高,这表明通过聚类成功地将具有相似功能连接特性的受试者分组,从而增强了分类过程。此外,所提出的混合方法确保了实际可行的再训练过程,提高了疲劳检测系统在实际应用中的适应性和有效性。
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