## 3 Results  
Self-reporting plays an important role in the physiological indicators of driving fatigue. Figure 2 presents the variation in participants' SSS scores. The results illustrate that participants were not fatigued before the real driving experiments and were quite fatigued after the real driving experiments. The SSS scores increased significantly from 1.8 to 5 (t = \(-\)12.829, \(p\) < 0.01, df = 9) after the real driving experiments. Subjective indicators reveal that a three-hour continuous real driving experiment does lead to an increase in fatigue.  
In the present work, the feature considered, namely SampEn, was used directly on the preprocessed EEG data for each channel from 10 participants. Figure 3 presents the mean and variance of the SampEn in the different channels for 1800 normal and 1800 fatigue EEG data. The y-axis represents the value of SampEn, and the x-axis is for the EEG channels (1, AF3; 2, F7; 3, F3; 4, FC5; 5, T7; 6, P7; 7, O1; 8, O2; 9, P8; 10, T8; 11, FC6; 12, F4; 13, F8; 14, AF4). From Figure 3, it can be seen that the SampEn dissimilarity varies among different channels. More specifically, compared to the normal state, a lower SampEn value for almost all channels in the fatigue state can be observed in Figure 3.  
The SampEn was given as input to the different classifiers considered in the present work. Ten-fold cross-validation was adopted to ensure the reliable classification performance of these classifiers. Specifically, the 1800 normal and 1800 fatigue EEG data were divided into ten sub-datasets. Each time, 1 of the 10 sub-datasets was marked as a test dataset, and the other 9 sub-datasets were put together to form a training dataset; the method was repeated 10 times. Finally, the average accuracy for all of the 10 times was calculated. Table 1 lists the performance analysis of these machine learning algorithms considered in this work on the testing dataset. Figure 4 shows 3D confusion matrices for the SampEn feature approach using multiple machine learning algorithms. Classification performance is affected by kernel functions [15]; therefore, we explored multiple classification algorithms (K nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR)) with different kernel functions. From Table 1 and Figure 4, we can see that the detection accuracies of the EEG records based on the SampEn feature vary from 90.0% to 97.2%. Particularly, the proposed methodology achieves the highest accuracy of 97.2% with the cubic SVM classifier. The best performance result is indicated in bold. It can be observed that 95.6% of the fatigue EEG features are correctly classified as fatigue states.  
Figure 2: The variation in participants’ SSS scores in different experimental conditions.  
Moreover, a small percentage of 4.4% of the normal EEG features are wrongly classified as fatigue states. Additionally, a high percentage of 98.9% of the normal EEG features are correctly classified as normal states and a small percentage of 1.1% of the fatigue EEG features are wrongly classified as normal states. From Figure 5, it can be seen that the proposed methodology achieves the highest sensitivity (SEN) of 95.6%, highest precision (PRE) of 98.9%, and highest specificity (SPE) of 98.9% for the cubic SVM classifier. In addition, the quadratic SVM classifier also shows good performances, with ACC, PRE, SEN, and SPE being 96.4%, 97.7%, 95.0%, and 97.8%, respectively, with the SampEn feature. It detected 171 true positives, 9 false negatives, 4 false positives, and 176 true negatives.