## 4 Results  
The performance of six different machine learning models was evaluated using both feature selection methods and without feature selection for the depression detection task using EEG data. The results were analyzed using the key metrics discussed earlier: accuracy, precision, recall, and F1 score. Table 2, which is sorted by model performance, presents a detailed summary of these metrics for each model across various feature selection techniques, providing a clear view of how the selection method impacts performance. The SVM-RFE method produced the best overall performance, particularly for the SVM and Logistic Regression. The SVM achieved an accuracy of 93.54%, with very high precision (94.68%) and recall (95.97%), resulting in an F1 score of 95.29%. Logistic Regression also performed well, achieving 92.86% accuracy. The success of RFE for these models is due to its iterative nature, where it removes the least important features and retrains the model on the remaining features. This method is especially effective for linear models when the feature space is high-dimensional, such as EEG data, because it eliminates redundant or irrelevant features while preserving the key signals that are most important. However, tree-based models like Random Forest and XGBoost performed significantly lower than the linear models. This reduced performance could be due to RFE's feature-removal process, which may disrupt the internal structure of tree-based models. These models are inherently designed to handle feature redundancy and importance internally, relying more on complex feature interactions than strict feature elimination, which explains their lesser dependence on external feature selection methods [57]. With Elastic Net as the feature selection method, both the SVM and Logistic Regression achieved the highest accuracy (90.47%), with almost identical precision and recall values, resulting in very high F1 scores (93.00%). This indicates that Elastic Net is particularly effective for linear models, as it balances feature selection and regularization properties, which prevents overfitting and ensures the models focus on the most relevant features, particularly in high-dimensional EEG data. Tree-based models like Random Forest and XGBoost saw a performance boost compared to their results without feature selection, with Random Forest reaching 77.87% accuracy and XGBoost achieving 77.56%. However, their low performance compared to the linear ones indicates that tree-based models may have lost some key features during the Elastic Net regularization process. With mutual information, non-linear models like Random Forest (77.53% accuracy) and LGBM (76.18% accuracy) performed better than linear models such as the SVM (65.63%) and Logistic Regression (66.99%). This is because mutual information captures non-linear relationships between features, which non-linear models can take advantage of. In contrast, linear models struggle to use these complex interactions, leading to lower performance. Non-linear models handle these relationships more effectively, giving them an advantage in this case. mRMR also favored non-linear models, with Random Forest achieving the highest accuracy (76.17%) and F1 score (82.86%). LGBM and XGBoost also performed well with mRMR, showing that non-linear models can leverage complementary features to build stronger predictive power. However, linear models like the SVM and Logistic Regression struggled with this method, as they rely more on direct feature relevance rather than interactions between features. The chi-squared feature selection approach yielded the best results for Random Forest, which achieved 75.51% accuracy, with a high F1 score of 82.53%. Tree-based models, such as CatBoost and LGBM, also performed reasonably well. However, the SVM and