Abstract
Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity of the EEG signal is then quantified with the sample entropy method. Finally, we explore the performance of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals. Experimental results show that the highest classification accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 98.9%, a precision of 98.9%, and the highest AUC value of 1 are achieved using SampEn feature and cubic SVM classifier (SCS Model). It is hence concluded that SampEn is an effectively distinguishing feature for classifying normal and fatigue EEG signals. The proposed system may provide us with a new and promising approach to monitoring and detecting driver fatigue at a relatively low computational cost.
Highlights
The results illustrate that that participants were not fatigued before the real driving experiments and were quite participants were not fatigued before the real driving experiments and were quite fatigued fatigued after the real driving experiments
Subjective indicators reveal reveal that a three-hour continuous real driving experiment does lead to an increase in that a three-hour continuous real driving experiment does lead to an increase in fatigue
In this study, we developed a fatigue detection system using the SampEn feature from 14 channels and seven classifiers, namely LR, linear SVM, quadratic SVM, cubic SVM, fine KNN, medium KNN, and cubic KNN, during real driving based on EEG signals
Summary
The aim of this study is to develop an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals
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