Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Engineering, Mamaghan Branch, Islamic Azad University, Mamaghan, Iran.
Med Biol Eng Comput. 2024 Apr;62(4):1017-1030. doi: 10.1007/s11517-023-02983-z. Epub 2023 Dec 20.
Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.
驾驶员疲劳是社会中的一个重大问题，根据组织报告，它是导致事故的主要原因之一。因此，准确检测驾驶员的疲劳状态对于减少因事故导致的人员死亡或受伤人数至关重要。已经提出了几种驾驶员疲劳识别方法，其中脑电图 (EEG) 是其中之一。本文提出了一种基于 EEG 信号的疲劳识别方法，该方法从源和传感器空间中提取特征。该方法首先通过应用滤波和去除伪迹进行预处理。然后，应用源定位方法对 EEG 信号进行主动源提取。对选定的源拟合多元自回归 (MVAR) 模型，并应用双卡尔曼滤波器估计源活动及其关系。然后在 EEG 和源活动信号之间拟合多元自回归移动平均 (ARMA) 模型。从模型参数、源关系矩阵以及 EEG 和源活动信号的小波变换中提取特征。该方法的新颖之处在于使用 ARMA 模型在源活动（作为输入）和 EEG 信号（作为输出）之间，以及从源关系中提取特征。使用 RelifF 和邻域成分分析 (NCA) 方法的组合选择相关特征。采用 k-最近邻 (KNN)、支持向量机 (SVM) 和朴素贝叶斯 (NB) 分类器这三种分类器来对驾驶员进行分类。为了提高性能，使用投票方法将这些分类器组合起来计算疲劳检测的最终标签。结果表明，与其他方法相比，该方法使用集成分类器可以准确识别和分类疲劳驾驶员。
