###### 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.  
EEG; real driving; fatigue +
Footnote †: journal:  
Fatigue is one of the crucial factors resulting in a large proportion of crashes in road driving in the transportation industry [11; 12]. Currently, existing fatigue-detection technologies mainly rely on driving behavior, vehicle driving information, and the driver's physiological characteristics [13; 14; 15]. Among these three technologies, driving fatigue is highly linked to physiological signals [13].  
For developing a driver fatigue detection system, in general, EEG is the most common noninvasive way to evaluate driver fatigue and also is a low-cost network of measurement compared to recent electrical neuroimaging modalities [14; 15]. In recent years, the method proposed in [16] classified the driver distraction level based on the fusion of discrete wavelet packet transform and FFT and three classifiers, namely subtractive fuzzy clustering, probabilistic neural network, and K nearest neighbor, using wireless EEG signals, obtaining the highest classification accuracy of 85%. In another study [17], the authors collected 16 channels of EEG signals and then calculated 12 types of energy parameters as features and finally employed the regression equation for the classification of fatigue and nonfatigue signals from the Fp1 channel and O1 channel, and they reported an accuracy of 91.5%. Time, spectral, wavelet analysis and neural network classifier were proposed to detect the drowsiness stage in EEG records in [18]; this method achieved accuracies of 87.4% and 83.6% for alertness and drowsiness, respectively.  
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. A novel system combining sample entropy and multiple machine learning algorithms is proposed. The novelty of this study can be summarized as follows: (1) development of an efficient system to determine whether a driver is fatigued during real driving (recognizing the state of the driver in a real driving environment it is a challenging and rewarding task); (2) development of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals; (3) achievement of high identification performance at relatively low computational cost (the highest classification accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 95.7%, a precision of 98.9%, the highest AUC value of 1, and a training time of 1.225 s with cubic SVM algorithm).  
The rest of the paper is organized as follows: Section 2 provides a detailed description of methods, including experiment design and data collection. The results and discussion of the experiment are shown in Section 3. Finally, the conclusion is presented in Section 4.