## 5 Discussion
Figure 7: Graph comparing the four performance indicators of feature selection methods on the DEAP dataset: (**a**) filter-based feature selection method; (**b**) embedded-based feature selection method; (**c**) wrapper-based feature selection method. Filter-based feature selection using SelectKBest with an ANOVA \(F\)-test, the initial \(F\)-test assessment yielded impressive baseline results, demonstrating the potential of the models. Subsequent evaluations of different numbers of features provided insights into the impact on accuracy. Notably, machine learning models with 100, 500, 1000, 1500, and 2000 features for the EEG Emotion dataset and 50, 100, and 150 features for the DEAP dataset demonstrated robustness, emphasizing the importance of finding the feature selection. The precision, recall, and F1 score considerations underscore the trade-offs in feature selection. The findings highlight the significance of systematic feature selection, ensuring both accuracy and efficient model deployment. Additionally, the time complexity of the ANOVA \(F\)-test is O(\(nm\)), where \(n\) represents the number of samples and \(m\) represents the number of features. This results in fast feature selection, but it may offer slightly lower machine learning's performance compared to other feature selection methods. Future research directions may involve exploring alternative feature selection methods and their impact on interpretability. The results of embedded-based feature selection using LASSO indicated nuanced changes in performance, with RF experiencing a slight decrease in accuracy, emphasizing the trade-off between feature reduction and model complexity. Logistic regression and SVM showed improvements, suggesting the efficacy of LASSO in enhancing certain models. The application of LASSO showed potential for achieving streamlined feature sets with maintained or improved model performance for both datasets in this study. The time complexity of feature selection using LASSO is O(\(nm\)k), where \(n\) represents the number of samples, \(m\) represents the number of features, and \(k\) indicates the number of iterations until convergence. As k decreases, this method approaches that of the ANOVA \(F\)-test. Additionally, since this method incorporates machine learning techniques, it generally yields favorable performance after feature selection. Future research could develop into optimizing the threshold for feature selection and exploring the application of LASSO in diverse datasets. Wrapper-based feature selection using GA for classification tasks demonstrated its efficacy in tailoring feature subsets for enhanced model performance. Baseline assessments without feature selection established benchmarks, and the GA showed its ability to optimize feature subsets for each model. Reductions in the number of features demonstrated the algorithm's success in capturing informative features while maintaining or improving key metrics. Our findings highlight the necessity of aligning algorithm selection with the available feature space and application requirements. For tasks where a vast feature space is accessible, algorithms such as RF and XGBoost tend to excel. The time complexity of feature selection using GA is O(\(pgt\)), where \(p\) represents the number of populations, \(g\) represents the number of generations, and \(t\) indicates the time complexity of the model training for evaluating each subset. This method generally requires more computational complexity compared to other feature selection methods. However, it has the advantage of potentially achieving better performance, especially when the search space is larger. Table 9 presents a comparison of different methods in the field of EEG Emotion data classification. In a comparison of EEG emotion analysis methods, notable studies by [PERSON] et al. (LSTM), [PERSON] et al. (Info Gain Feature Selection and RF), and [PERSON] et al. (2D DevoCNN method for KLD) have demonstrated diverse approaches with impressive accuracies ranging from 97.00% to 98.59%.