## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject ID**} & \multirow{2}{*}{**LR**} & **Linear** & **Quadratic** & **Cubic** & **Fine** & **Medium** & **Cubic** \\  & & **SVM** & **SVM** & **SVM** & **KNN** & **KNN** & **KNN** \\ \hline
1 & 89.4\% & 90.0\% & 93.6\% & 93.1\% & 98.3\% & 94.4\% & 94.7\% \\
2 & 90.6\% & 91.1\% & 93.3\% & 95.8\% & 97.8\% & 94.2\% & 93.9\% \\
3 & 92.5\% & 92.8\% & 95.8\% & 96.9\% & 97.8\% & 95.8\% & 95.6\% \\
4 & 90.8\% & 90.8\% & 93.9\% & 95.6\% & 98.6\% & 93.9\% & 93.6\% \\
5 & 90\% & 90.8\% & 91.9\% & 93.6\% & 97.8\% & 92.8\% & 92.2\% \\
6 & 90.6\% & 91.1\% & 93.3\% & 95.6\% & 97.8\% & 94.2\% & 93.9\% \\
7 & 90.8\% & 92.5\% & 93.9\% & 94.4\% & 97.2\% & 94.7\% & 95.0\% \\
8 & 91.7\% & 91.7\% & 92.8\% & 95\% & 97.5\% & 94.4\% & 93.9\% \\
9 & 91.1\% & 91.4\% & 93.9\% & 95.8\% & 97.8\% & 94.2\% & 93.3\% \\
10 & 89.4\% & 93.1\% & 93.1\% & 95.6\% & 97.8\% & 93.9\% & 94.4\% \\ Average & 90.69\% & 91.53\% & 93.55\% & 95.14\% & 97.8\% & 94.25\% & 94.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of leave-one-subject-out fatigue recognition. Figure 6: ROC curves and AUCs (the areas under ROC curves): (**a**) cubic SVM; (**b**) quadratic SVM. For a valid comparison, we used the same experimental data to compare the method considered in the present work with other published methods in terms of classification performance. We adopted the method proposed in [16], using probabilistic neural network and K nearest neighbor for different wavelets (fusion of discrete wavelet packet transform and FFT), and achieved classification accuracy rates of 92% and 89%, respectively. We also attempted the method proposed in [17], calculated 12 types of energy parameters as features, and finally used the regression equation for the classification of fatigue and nonfatigue signals from the O1 channel; the accuracy was 93%. Finally, we used time, spectral, wavelet analysis and neural network classifier, as introduced in [18], and obtained an accuracy of 89.5%. In the present work, we developed a fatigue detection system using SampEn feature and cubic SVM classifier (SCS Model) during real driving based on EEG signals, which gives the highest accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 95.6%, and a specificity of 95.6%. Figure 8: Performance measures of leave-one-subject-out fatigue recognition: (**a**) sensitivity; (**b**) specificity; (**c**) precision. Figure 7: Confusion matrices for leave-one-subject-out fatigue recognition with fine KNN. 98.9%, a precision of 98.9%, and the highest value of area under the receiver operating curve (AUC = 1). In addition, compared with current EEG-based fatigue-detection technologies, our system only uses the SampEn feature. The merit of our proposed system is that it is very effective for the analysis of short-length time series and achieves high detection performance with low computational cost. For the deployment of the fatigue detection system proposed in this research, the driver's EEG signals are continuously collected by EMOTIV EPOC and then wirelessly transmitted to an external laptop via the USB receivers for the SCS model. Finally, it is judged whether the driver is in a state of fatigue.