## 1 Introduction  
Driver fatigue has become one of the major causes of traffic accidents globally. However, it is a complicated physiological process which is gradual and continuous, so to date there is no effective method to detect the driving fatigue. For driver fatigue detection, physiological signals in electroencephalography (EEG), electroocologram (EOG), sweat, saliva and voice have been all investigated. Though functional magnetic resonance imaging (fMRI) was widely used to study the operational organization of the human brain (with considerable clinical significance), it could imply high expense and operate inconveniently for driving fatigue in real driving conditions [1]. Recently, a relatively new classification techniques for functional near-infrared spectroscopy (fNIRS) was also widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation, it has low cost, portability, safety, low noise (compared to fMRI), and ease of use [2; 3], For example, [PERSON] used fNIRS to discriminate the alert and drowsy states for a passive brain-computer interface, obtaining average accuracies in the right dorsolateral prefrontal cortex of 83.1%, 83.4% and 84.9% in different time windows respectively [4]. However, fNIRS is mainly at present a confirmatory study with shortcomings of poor time resolution compared with EEG/ERP (event-related potential) and signal acquisition without covering the whole brain. Comparatively speaking, EEG is most common non-invasive way to identify driver fatigue. A lot of related research in this field had been published. [PERSON] et al. [5] used EEG alpha principal axis measurement in real traffic situation as the driver fatigue index. They found that, compared with EEG power, the alpha principal parameters gave better fatigue test sensitivity as well as specificity. [PERSON] et al. [6] achieved a success rate of 84.8% in the use of empirical mode decomposition (EMD) to process the EEG signal for fatigue detection. [PERSON] et al. [7] used discrete wavelet transforms (DWT) to process the EEG signal for fatigue detection. DWT and fast Fourier transformation (FFT) methods were combined to correlate the distracted, fatigue, alert states corresponding to alpha-, delta-, theta- and beta-wave features, before db4, db8, sym8, coif5 wavelet transforms were performed on the EEG bands. The db4 wavelet transform yielded the highest accuracy of 85%. [PERSON] et al. [8] studied the automated fatigue detection system with EEG-based multi-mode analysis. They found that there are 19 features for differentiation in the signal from just a single EEG channel. Based on the Wiles test, feature indices were fed into neural network classifier. A total of 18 EEG signals were analyzed with this method, and achieved an accuracy of 83.6%. The discrimination accuracy was as high as 94.25%. Steady-state visual evoked potential (SSVEP) was applied in the study by [PERSON] [9], who used different scan times in the optical stimulation on drivers. Two Fourier transforms were used in the feature extraction and three different linear discriminant analysis classifiers to obtain an accuracy of 98.20%. In the EEG signal analysis, information entropies, such as fuzzy entropy, sample entropy, approximate entropy, wave entropy, power spectrum entropy and sort entropy, are often used as entropy-based feature extraction method [10; 11; 12; 13]. These entropies are often used for quantification in the cognitive analysis of EEG signals in different mental state and sleep state, indicating that entropy index is a rather useful tool for EEG analysis. In this paper, spectrum entropy, approximate entropy, sample entropy and fuzzy entropy are all used for EEG signals collected in normal (rest) state and fatigue driving states in order to extract features. Current research indicates that different calculation methods about entropy features have different advantages.