Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model

• Published in: Cognitive neurodynamics Vol. 12; no. 4; pp. 431 - 440
• Main Authors: Hu, Jianfeng, Min, Jianliang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2018; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brief Communication; Classification; Cognitive Psychology; Computer Science; Decision trees; Discriminant analysis; Driver fatigue; EEG; Electrocardiography; Electroencephalography; Entropy; Experiments; Fatigue; Methods; Neural networks; Neurosciences; Physiology; Questionnaires; Signal classification; Software; Support vector machines; Traffic accidents; Driver fatigue; Gradient boosted decision tree (GBDT); Entropy; Electroencephalogram (EEG)
• ISSN: 1871-4080; 1871-4099; 1871-4099
• DOI: 10.1007/s11571-018-9485-1

Driver fatigue is increasingly a contributing factor for traffic accidents, so an effective method to automatically detect driver fatigue is urgently needed. In this study, in order to catch the main characteristics of the EEG signals, four types of entropies (based on the EEG signal of a single channel) were calculated as the feature sets, including sample entropy, fuzzy entropy, approximate entropy and spectral entropy. All feature sets were used as the input of a gradient boosting decision tree (GBDT), a fast and highly accurate boosting ensemble method. The output of GBDT determined whether a driver was in a fatigue state or not based on their EEG signals. Three state-of-the-art classifiers, k-nearest neighbor, support vector machine and neural network were also employed. To assess our method, several experiments including parameter setting and classification performance comparison were performed on 22 subjects. The results indicated that it is possible to use only one EEG channel to detect a driver fatigue state. The average highest recognition rate in this work was up to 94.0%, which could meet the needs of daily applications. Our GBDT-based method may assist in the detection of driver fatigue.