###### Abstract  
Major depressive disorder (MDD) poses a significant challenge in mental healthcare due to difficulties in accurate diagnosis and timely identification. This study explores the potential of machine learning models trained on EEG-based features for depression detection. Six models and six feature selection techniques were compared, highlighting the crucial role of feature selection in enhancing classifier performance. This study investigates the six feature selection methods: Elastic Net, Mutual Information (MI), Chi-Square, Forward Feature Selection with Stochastic Gradient Descent (FFS-SGD), Support Vector Machine-based Recursive Feature Elimination (SVM-RFE), and Minimal-Redundancy-Maximal-Relevance (mRMR). These methods were combined with six diverse classifiers: Logistic Regression, Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). The results demonstrate the substantial impact of feature selection on model performance. SVM-RFE with SVM achieved the highest accuracy (93.54%) and F1 score (95.29%), followed by Logistic Regression with an accuracy of 92.86% and F1 score of 94.84%. Elastic Net also delivered strong results, with SVM and Logistic Regression both achieving 90.47% accuracy. Other feature selection methods yielded lower performance, emphasizing the importance of selecting appropriate feature selection and machine learning algorithms. These findings suggest that careful selection and application of feature selection techniques can significantly enhance the accuracy of EEG-based depression detection. d 2024 14 10532 10.1000 10.  
high cost of MRI [1]. The other approach gaining attention is using Electroencephalography (EEG), which records brain electrical activity. Given depression's neurological impact, researchers worldwide are actively seeking improved EEG-based biomarkers for diagnosis. Recent studies have shown that EEG-based depression detection is a promising new approach because EEG is less complex, more cost-effective, and more patient-friendly than MRI [2]. Machine and deep learning models [3] show promise for improving depression detection, yet several important challenges must be addressed before they can be widely used. One challenge is ensuring the quality of the data used to train these models. It is crucial to use diverse and reliable data. Another challenge is model interpretability--patients and doctors should understand how these models make predictions. Additionally, addressing biases in these models is essential, as biased results may lead to incorrect diagnoses. Despite these challenges, machine learning has the potential to revolutionize the way that depression is detected and diagnosed. With further research and development, machine learning models could be used to improve early detection of depression and lead to better treatment outcomes. This research addresses a significant challenge in mental healthcare, depression detection, emphasizing the need for a more objective, data-driven approach. By exploring the potential of machine learning models trained on EEG-based features, this study compares six models and six feature selection techniques. The primary contribution lies in a comprehensive evaluation of these feature selection techniques across the models, providing critical insights into the relationship between feature selection and model performance, particularly in the context of EEG-based depression detection. This research highlights the critical role of feature selection in optimizing classifier performance, even with hyperparameter tuning, furthering the field of EEG-based depression detection and underlining the importance of meticulous feature selection for maximizing model effectiveness. This paper is organized into key sections as follows: the Background section introduces the existing state of the field.