Globally, 14%–20% of road accidents are mainly due to driver fatigue, the causes of which are instance sickness, travelling for long distance, boredom as a result of driving along the same route consistently, lack of enough sleep, etc. This article presents a flexible analytic wavelet transform (FAWT)-based advanced machine learning method using single modality neurophysiological brain electroencephalogram signals to detect the driver fatigues (i.e., FATIGUE and REST) and to alarm the driver at the earliest to prevent the risks during driving. First, signals of undertaking study groups are subjected to the FAWT that separates the signals into LP and HP channels. Subsequently, relevant subband frequency components with proper setting of tuning parameters are extracted. Then, comprehensive low order features which are statistically significant for $p< 0.05$, are evaluated from the input subband searched space and embedded them to various ensemble methods under multiboost strategy. Results are evaluated in terms of various parameters including accuracy, F-score, AUC, and $\kappa$. Results show that the proposed approach is promising in classification and it achieves optimum individual accuracies of 97.10% and 97.90% in categorizing FATIGUE and REST states with F-score of 97.50%, AUC of 0.975, and $\kappa$ of 0.950. Comparison of the proposed method with the prior methods in the context of feature, accuracy, and modality profiles undertaken, indicates the effectiveness and reliability of the proposed method for real-world applications.

中文翻译：

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使用 FAWT 和 Multiboosting 方法的基于 EEG 的驾驶员疲劳检测

在全球范围内，14%~20% 的道路事故主要是由于驾驶员疲劳造成的，其原因包括突发疾病、长途旅行、因始终沿同一路线行驶而感到无聊、睡眠不足等。文章提出了一种基于灵活分析小波变换（FAWT）的先进机器学习方法，利用单模态神经生理脑脑电图信号检测驾驶员疲劳（即疲劳和休息），并尽早警告驾驶员以预防驾驶过程中的风险。首先，承接研究组的信号经过 FAWT，将信号分为 LP 和 HP 信道。随后，通过适当设置调谐参数，提取相关子带频率分量。然后，综合低阶特征在统计上显着$p< 0.05$，从输入子带搜索空间进行评估，并将它们嵌入到多重提升策略下的各种集成方法中。结果根据各种参数进行评估，包括准确性、F 分数、AUC 和$\卡帕$. 结果表明，所提出的方法在分类方面很有前景，它在对疲劳和休息状态进行分类时达到了 97.10% 和 97.90% 的最佳个体准确率，F 分数为 97.50%，AUC 为 0.975，并且$\卡帕$0.950。在所采用的特征、准确性和模态配置文件的背景下，将所提出的方法与现有方法进行比较，表明所提出的方法在实际应用中的有效性和可靠性。