<![CDATA[Detecting depression and identifying its severity remain challenging tasks, especially in diverse environments where fair and reliable outcomes are expected. This study aims to address this problem with advanced machine learning models to achieve high accuracy and explainability; making the approach suitable for the real world depression screening and stage evaluation by implementing EEG-based depression detection and staging. We established the parameters of development of EEG-based depression detection in optimization of channel selection together with machine-learning models. Extreme channel selection was performed during this study with Recursive Feature Elimination (RFE) whereby major 11 channels identified, and the MLP classifier achieved 98.7% accuracy supported by AI explainability, thus outpacing the XGBoost and LGBM by 5.2 to 8.2% across multiple datasets (n=184 to 382) and greatly endorsed incredible generalization (precision=1.000, recall=0.966). This makes MLP a trustworthy BCI tool for real-world implementation of depression screening. We also examined assigning depression stages (Mild/Moderate/Severe) on EEG data with models supported or not with GAN-based augmentation (198 to 5,000 samples). CNNs did well on Moderate-stage classification, while ANFIS kept a firm accuracy of 98.34% at perfect metric consistency (precision/recall=0.98) with AI explainability. GAN augmentation improved the classifications of severe cases by 15%, indicating a good marriage of neuro-fuzzy systems and synthetic data for the precise stage determination. This is an important contribution to BCI research since it offers a data-efficient and scalable framework for EEG based depression diagnosis and severity evaluation, thus contributing to the bridge between competitive modeling and clinical applicability. This work, therefore, lays down a pathway for the design of accessible and automated depression screening aids in both high-resource and low-resource settings]]>
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