## 4 Classification Effect on the Dataset
### Classification Effect on Five Classification Datasets

Table 4: Model evaluation indicators in the five-class classification task. Figure 16: The ROC curve of the XGBoost model in the five-class classification task. Figure 17: Accuracy % of the top ten models for five classification tasks \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Model Type** & **Accuracy (\%)** & **Total Cost** & **Preset** & **Prediction Speed (obs/s)** & **Training Time (s)** \\ \hline
1 & SVM & 82.0 & 90 & Custom SVM & 10,505.95 & 249.81 \\
2 & SVM & 81.2 & 94 & Linear SVM & 3907.47 & 4.57 \\
3 & Ensemble & 81.0 & 95 & Custom Ensemble & 3021.87 & 88.07 \\
4 & SVM & 80.8 & 96 & Quadratic SVM & 3794.01 & 4.37 \\
5 & Neural Network & 80.6 & 97 & Custom Neural Network & 21,322.14 & 261.35 \\
6 & Neural Network & 80.6 & 97 & Medium Neural Network & 10,889.95 & 4.83 \\
7 & Neural Network & 80.0 & 100 & Wide Neural Network & 22,057.92 & 2.10 \\
8 & Ensemble & 79.2 & 104 & Bagged Trees & 2281.52 & 5.26 \\
9 & SVM & 79.0 & 105 & Cubic SVM & 3244.70 & 4.26 \\
10 & SVM & 78.6 & 107 & Medium Gaussian SVM & 2875.67 & 4.03 \\
11 & Ensemble & 78.2 & 109 & Boosted Trees & 2966.54 & 3.95 \\
12 & Tree & 77.8 & 111 & Fine Tree & 5292.95 & 8.34 \\
13 & Tree & 77.8 & 111 & Custom Tree & 8286.84 & 25.31 \\
14 & Neural Network & 77.8 & 111 & Narrow Neural Network & 13,850.45 & 5.43 \\
15 & Neural Network & 77.6 & 112 & Trilayered Neural Network & 14,166.03 & 7.04 \\
16 & Neural Network & 77.4 & 113 & Bilayered Neural Network & 13,540.19 & 5.83 \\
17 & KNN & 76.8 & 116 & Custom KNN & 6235.37 & 26.83 \\
18 & Kernel & 76.4 & 118 & Custom Kernel & 5609.94 & 10.26 \\
19 & Ensemble & 74.8 & 126 & Subspace Discriminant & 1279.07 & 4.06 \\
20 & KNN & 74.6 & 127 & Weighted KNN & 4769.25 & 2.09 \\
21 & Kernel & 74.6 & 127 & Custom Kernel & 5119.61 & 5.70 \\
22 & Tree & 74.4 & 128 & Medium Tree & 11,200.87 & 0.97 \\
23 & Ensemble & 74.2 & 129 & RUSBoosted Trees & 2949.14 & 3.89 \\
24 & Ensemble & 73.8 & 131 & Subspace KNN & 893.56 & 5.70 \\
25 & Discriminant & 72.2 & 139 & Optimizable Discriminant & 7123.51 & 30.54 \\
26 & Discriminant & 72.2 & 139 & Linear Discriminant & 11,343.24 & 3.04 \\
27 & Naive Bayes & 71.6 & 142 & Custom Naive Bayes & 967.91 & 111.53 \\
28 & Naive Bayes & 71.6 & 142 & Custom Naive Bayes & 822.89 & 7.32 \\
29 & KNN & 71.6 & 142 & Fine KNN & 5459.71 & 3.28 \\
30 & KNN & 71.0 & 145 & Cosine KNN & 4489.38 & 2.32 \\
31 & SVM & 70.4 & 148 & Coarse Gaussian SVM & 5785.77 & 5.28 \\
32 & KNN & 70.0 & 150 & Medium KNN & 4529.33 & 1.64 \\
33 & KNN & 69.0 & 155 & Cubic KNN & 3553.80 & 2.20 \\
34 & Naive Bayes & 67.0 & 165 & Gaussian Naive Bayes & 2288.75 & 2.76 \\
35 & SVM & 67.0 & 165 & Fine Gaussian SVM & 2990.76 & 4.15 \\
36 & Tree & 65.2 & 174 & Coarse Tree & 11,090.56 & 3.89 \\
37 & KNN & 58.6 & 207 & Coarse KNN & 4593.06 & 1.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of different classifiers in five classification tasks. We compared the method in this paper with the popular EEG classification model CNN method based on deep learning, and from Figure 18, we found that the test set accuracy of the CNN method on the five classification task was 0.67, which was not as high as the accuracy of the method proposed in this paper. This can illustrate the effectiveness of our method. The confusion matrix under the CNN method is shown in Figure 19. Therefore, our framework performs well across all indicators of the five classification tasks. From Table 5, it can be seen that, using XGBoost, the framework performs well across various metrics in the five-class classification task, with the highest classification accuracy achieved. Figure 19: Confusion matrix diagram of CNN model in the five-class classification task. Figure 18: Accuracy of the CNN model in the five-class classification task.