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Dynamic reward-augmented ensemble learning for EEG signal classification in major depressive disorder
Jin Xu1, Yu Ziwei1, Xu Zhaojun2
1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, People's Republic of China.
Biomedical Physics & Engineering Express
|December 10, 2025
Summary
This study introduces an Adaptive Agent-Based Ensemble Learning (AABEL) framework for diagnosing Major Depressive Disorder (MDD) using electroencephalography (EEG). AABEL enhances diagnostic accuracy and robustness by dynamically adapting model contributions through reinforcement learning (RL).
Area of Science:
- Computational Neuroscience
- Neuroengineering
- Machine Learning for Healthcare
Background:
- Diagnosing Major Depressive Disorder (MDD) with electroencephalography (EEG) is challenging due to neural signal variability and limitations of static classification models.
- Conventional ensemble methods and monolithic deep learning architectures struggle with adaptability, generalizability, and handling inter-individual neurophysiological differences or artifacts in EEG data.
Purpose of the Study:
- To develop an Adaptive Agent-Based Ensemble Learning (AABEL) framework integrating reinforcement learning (RL) with neurocomputational principles for improved EEG-based MDD diagnosis.
- To overcome the inflexibility of static ensembles and enhance the generalizability of deep learning models in clinical EEG analysis.
Main Methods:
- Implemented RL-Driven Adaptive Weighting to dynamically adjust submodel (CNN, GRU, Transformer) contributions via reward signals.
- Employed Multiscale Neurodynamic Feature Fusion to extract complementary spatial-spectral, temporal-contextual, and global interdependency features from EEG signals.
- Utilized End-to-End Reward Propagation for automated optimization, directly linking reward calculations to model weight updates.
Main Results:
- AABEL achieved superior classification performance on the OpenNeuro ds003478 dataset, with 98.06% accuracy and 98.20% F1-score, significantly outperforming static ensembles.
- The RL reward mechanism improved classification stability by 3.6% and enhanced robustness against noise.
- Demonstrated a 96% accuracy improvement over a Fuzzy Ensemble baseline.
Conclusions:
- The AABEL framework represents a novel paradigm for adaptive EEG-based MDD diagnostics by integrating dynamic reward-augmented learning with neurosignal processing.
- This approach offers a scalable and personalized framework for mental health monitoring, bridging computational neuroscience and translational neuroengineering.
