## 3 Experiment and Results
### Classification Result  
SVM classifier enjoys unique advantages over other classifiers in small-sample, non-linear and high-dimension pattern recognition, which is the case for the identification of the positive and negative sample described in this paper. Each of the entropies comes with its unique features. To obtain the most suitable features, we selected four entropies: spectrum entropy, approximate entropy, sample entropy and fuzzy entropy. For each type of entropy, certain electrodes are selected for SVM as the input feature. Figure 3 shows, in term of mean and variance, the four entropy values of a randomly selected subject. The \(x\)-axis is for the EEG label and \(y\)-axis denotes the entropy value. Both the mean and variance indicate that the entropy dissimilarity varies from different electrodes and that the fuzzy entropy has strong stability and obvious effect. In addition, from the Figure 3, it can be obviously observed that the sample entropy and the approximate entropy had similar entropy dissimilarity and the spectrum entropy had a weak dissimilarity. Figure 4 is the comparison of fisher distance on fuzzy entropy among multiple samples, which shows the obvious two-state sample feature difference between different electrodes. For 12 subjects, the \(t\) test is performed for the features of two type states with data from the whole electrode, the maximum \(p\) value reached 6 \(\times\) 10\({}^{-5}\). The results of Figures 3 and 4 show that the features of the two states based on fuzzy entropy have a significant difference under the criteria with the Fisher distance. Similarly, for each subject, if performed for the four combined entropy features of the two states with data from the same electrode, the \(p\) values with 27 electrodes in the international 10-20 system are  
Figure 2: Comparison between EEG signals in normal state and fatigue state in the time domain. (0.18, 0.34, 0.68, 0.30, 0.77, 0.60, 0.76, 0.33, 0.22, 0.21, 0.57, 0.19, 0.77, 0.49, 0.53, 0.26, 0.24, 0.16, 0.45, 0.48, 0.34, 0.14, 0.11, 0.60, 0.32, 0.34, 0.68) \(\times\) 10\({}^{-3}\), which obtained the result that the T5, TP7, TP8 and FP1 electrodes had a significant difference. In addition, Figure 5 shows a comparison with the combined entropy between the normal state and fatigue state, which selected 30 electrodes sorted by the Fisher distance according to the descending order. Figure 4: The comparison of Fisher distance on fuzzy entropy among multiple samples in two states. Figure 3: The value of mean and variance in two states with respect to the four types of entropy. The classification performance thus obtained by the jackknife test using fusion of the four types of entropies are given in Table 1, from which we can see that FuzzyEn-based method owns maximum classification accuracy, using the SVM classifier with the RBF function. The parameter \(Sn\) indicates the ability of the classifier to accurately identify the proportion of true positive samples from the test set. Similarly, the parameter \(Sp\) indicates the ability of the classifier to accurately identify the proportion of true negative samples from the test set. In this work, we have achieved the highest \(Sn\) and \(Sp\) of 91.50% and 92.50% for the FuzzyEn, respectively. It appears that the FuzzyEn-based method exhibits a remarkably higher accuracy, sensitivity and specificity than that of the SampleEn-based or else methods. In terms of classification accuracy with respect to entropy, the fuzzy entropy and sample entropy shows a stable performance for RBF kernel function and is suitable for the EEG-signal classification. [PERSON]'s correlation coefficient (MCC) evaluates the classification accuracy of imbalanced positive and negative samples in a dataset. The highest value of the MCC parameter, 85.02% and 78.66%, also indicates when the radial basis function is used as the kernel function of SVM, the characteristics of sample entropy and fuzzy entropy are obvious. The result indicates that the accuracy is improved with combined entropies.