## 5 Discussion  
Figure 6 shows the performance of four machine learning models via the three feature selection methods for classifying the EEG Emotion dataset. Evaluations of different numbers of features revealed their effects on accuracy and efficiency. Models using 100-2000 features for the EEG Emotion dataset proved robustness, highlighting the importance of feature selection. The precision, recall, and F1 score emphasize the trade-offs in feature selection, underscoring its role in accuracy and model efficiency. Future research could explore alternative feature selection methods and their interpretability. Embedded feature \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Features** & **Model** & **Labels** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline \multirow{8}{*}{160} & \multirow{2}{*}{RF} & Valence & 66.13 & 65.77 \\ \cline{3-5}  & & Arousal & 60.34 & 58.08 \\ \cline{3-5}  & & \multirow{2}{*}{Logistic Regression} & Valence & 60.90 & 60.20 \\ \cline{3-5}  & & \multirow{2}{*}{XGBoost} & Arousal & 63.18 & 57.90 \\ \cline{3-5}  & & \multirow{2}{*}{XGBoost} & Valence & 63.06 & 62.67 \\ \cline{3-5}  & & \multirow{2}{*}{Arousal} & Arousal & 60.68 & 58.49 \\ \cline{3-5}  & & \multirow{2}{*}{Vall} & Valence & 61.59 & 60.67 \\ \cline{3-5}  & & \multirow{2}{*}{Arousal} & Arousal & 63.06 & 57.08 \\ \hline \multirow{2}{*}{76} & \multirow{2}{*}{RF} & Valence & 64.20 & 63.71 \\ \cline{3-5}  & & \multirow{2}{*}{Arousal} & **62.15** & **59.88** \\ \cline{3-5}  & & \multirow{2}{*}{Arosic Regression} & Valence & **63.52** & **63.12** \\ \cline{3-5}  & & \multirow{2}{*}{Arousal} & **62.84** & **58.86** \\ \hline \multirow{2}{*}{70} & \multirow{2}{*}{XGBoost} & Valence & **64.65** & **64.05** \\ \cline{3-5}  & & \multirow{2}{*}{Arousal} & **63.52** & **61.43** \\ \cline{3-5}  & & \multirow{2}{*}{Vall} & Valence & **63.63** & **63.34** \\ \cline{3-5}  & & \multirow{2}{*}{Arousal} & **62.84** & **59.17** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Machine learning performance after applying the wrapper-based feature selection method. Improved results compared to models without feature selection are marked in boldface. selection with LASSO exhibited varied performance changes with all machine learning algorithms that showed better performance. LASSO shows promise for achieving efficient feature sets with stable or improved performance. Future research could optimize the feature selection thresholds and apply LASSO to diverse datasets. Wrapper-based feature selection using a GA proved effective in enhancing model performance by customizing feature subsets. Figure 7 shows the four machine learning performances using the three feature selection methods for classifying arousal and valence in the DEAP dataset. It reveals the  
Figure 6: Graph comparing the four performance indicators of feature selection methods on the EEG Emotion dataset: (**a**) filter-based feature selection method; (**b**) embedded-based feature selection method; (**c**) wrapper-based feature selection method. impact of different numbers of features on accuracy and efficiency. Models using 50, 100, and 150 features for the DEAP dataset were robust, highlighting the importance of feature selection. The precision, recall, and F1 scores emphasize the selection trade-offs. Future research should explore alternative methods for improving interpretability. LASSO-based feature selection showed nuanced performance changes: RF experienced a slight drop in accuracy, whereas logistic regression and SVM improved. LASSO proved to be effective for streamlined feature sets with stable or better performance. Future work could refine selection thresholds and apply LASSO to diverse datasets. GA-based wrapper selection enhanced model performance by optimizing feature subsets, reducing the number of features while maintaining metrics. Aligning the algorithm choice with the feature space is crucial.