## 4 Classification Effect on the Dataset
### Classification Effect on Five Classification Datasets  
In this article, the five-category classification of EEG signals can more finely distinguish between healthy states and different states of epilepsy. The five-category classification task helps doctors make more detailed divisions of patients' disease states, thereby formulating more personalized treatment plans. In epilepsy treatment, understanding the patient's specific seizure type and brain area activity is crucial to choosing appropriate drugs and surgical strategies. Through the five-category classification, we can more deeply analyze the differences and connections between different disease states and provide data support for the exploration of disease mechanisms and innovation of treatment methods. Therefore, the five-class classification was then performed. The evaluation metrics for both the single-method XGBoost model and our method obtained using Python are as follows in Table 4. The confusion matrix diagram and ROC curve of the XGBoost model on the five classification tasks are plotted in Figure 15 and Figure 16, respectively. Figure 14: Confusion matrix diagram of CNN method under binary classification task. Figure 15: The confusion matrix diagram of the XGBoost model under a five-class classification task. Although the classification accuracy of individual algorithms is relatively low, the performance metrics for the five-class classification are significantly improved after applying the feature concatenation method based on ANOVA. It is observed that our feature extraction framework performs well across various performance indicators when classified with the XGBoost model. The balanced accuracy is 0.824, demonstrating that the model performs consistently across all classes, with an overall accuracy of 82.4%. The specificity reaches 95.6%, indicating the model's strong ability to correctly identify non-target samples for each class. The \(F_{1}\) score is also 0.823, highlighting a good balance between precision and recall. Both the accuracy and recall rates stand at 0.824, showing the model's reliability in predicting positive samples and effectively identifying most of the actually positive cases. The precision value of 0.823 indicates that the model is highly accurate in predicting positive cases. The Matthews correlation coefficient (MCC) and Cohen's Kappa coefficient are both 0.780, suggesting that the model's prediction quality is significantly better than random guessing. The Hamming loss is 0.176, meaning 17.6% of the samples were misclassified, while the Jaccard index is 0.715, indicating a 71.5% similarity between the predicted and actual results. In summary, the XGBoost model demonstrates excellent performance across multiple metrics, proving to be both robust and effective on this dataset. A comparison of other machine learning methods using MATLAB's Classification Learner is presented as follows (after sorting) in Table 5. To make it easier to see which are the top 10 models of the Matlab R2023b classification toolbox in the five classification tasks, we have made a visualization in Figure 17. Therefore, our framework performs well on all indicators of the five classification tasks. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Method** & **Accuracy** & **Balanced Accuracy** & **F1 Score** & **Precision** & **Recall** & **Specificity** & **MCC** & **Cohen’s Kappa** & **Hamming Loss** & **Jaccard Index** \\ \hline VG & 0.568 & 0.568 & 0.571 & 0.576 & 0.568 & 0.892 & 0.460 & 0.460 & 0.432 & 0.406 \\ HVG & 0.728 & 0.728 & 0.728 & 0.729 & 0.728 & 0.932 & 0.660 & 0.660 & 0.272 & 0.592 \\ LPVG & 0.594 & 0.594 & 0.596 & 0.603 & 0.594 & 0.899 & 0.493 & 0.493 & 0.406 & 0.430 \\ MPDM & 0.470 & 0.470 & 0.469 & 0.469 & 0.470 & 0.868 & 0.338 & 0.338 & 0.530 & 0.309 \\ QG & 0.438 & 0.438 & 0.436 & 0.435 & 0.438 & 0.860 & 0.298 & 0.298 & 0.562 & 0.291 \\
**Ours** & **0.824** & **0.824** & **0.823** & **0.823** & **0.824** & **0.956** & **0.780** & **0.780** & **0.176** & **0.715** \\ \hline \hline \end{tabular}
\end{table}