Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis

This study introduces a novel adaptive deep learning framework for EEG-based schizophrenia diagnosis that addresses the limitations of existing static classification models. Traditional approaches often fail to maintain diagnostic reliability when EEG signal distributions shift over time due to patient variability, electrode noise, medication effects, or session-based changes. To overcome these challenges, we propose a drift aware integrating deep feature learning, concept-drift detection, and sliding-window-based sensitivity analysis. The system combines convolutional neural representations with an adaptive drift-monitoring module that continuously evaluates temporal changes in EEG patterns using incremental windows. When drift is detected, the model autonomously updates its parameters, ensuring stable performance during long-term or real-world clinical deployment. A comprehensive SOTA evaluation was conducted using several advanced augmentation and enhancement methods, including SMOTE and data-driven signal transformations. Among the compared models, NEXUS achieved the highest accuracy (98.12%) and best mean rank (1.25), followed by HYPR-A and ZETA-G. The proposed adaptive drift-aware architecture demonstrated superior robustness, lower variance, and stronger generalization under non-stationary EEG conditions compared to conventional deep learning frameworks. These findings confirm that integrating drift-adaptive learning with deep EEG feature extraction significantly enhances diagnostic precision, offering a clinically scalable solution for early and reliable schizophrenia detection.