Dynamic reward-augmented ensemble learning for EEG signal classification in major depressive disorder

Major Depressive Disorder (MDD) diagnosis through Electroencephalography (EEG) is hindered by the non-stationary characteristics of neural oscillations and the limited adaptability of conventional classification frameworks. Static ensemble models, which rely on predetermined weight assignments, exhibit suboptimal performance in handling EEG variability induced by inter-individual neurophysiological diversity or environmental artifacts. Meanwhile, monolithic deep learning architectures often suffer from inadequate generalizability in clinical practice. To overcome these limitations, we present an Adaptive Agent-Based Ensemble Learning (AABEL) framework that integrates reinforcement learning (RL) with neurocomputational principles. AABEL pioneers three methodological advancements: (1) RL-Driven Adaptive Weighting: A meta-controller dynamically adjusts the contributions of convolutional (CNN), recurrent (GRU), and attention-based (Transformer) submodels through task-oriented reward signals, resolving the inflexibility of static ensemble paradigms. (2) Multiscale Neurodynamic Feature Fusion: Parallel processing branches extract complementary representations of EEG signals, including spatial-spectral patterns (CNN), temporal-contextual dynamics (GRU), and global interdependencies (Transformer), enabling holistic modeling of neuropathological signatures. (3) End-to-End Reward Propagation: An automated optimization pipeline eliminates manual aggregation rules by directly linking reward calculations to model weight updates. Utilizing the OpenNeuro ds003478 dataset, AABEL achieves superior classification metrics (accuracy: 98.06%, F1-score: 98.20%), outperforming static ensembles (e.g., Fuzzy Ensemble by 96% accuracy). The RL reward mechanism significantly enhances noise robustness, improving classification stability by 3.6%. By integrating dynamic reward-augmented learning with neurosignal processing, AABEL establishes a new paradigm for adaptive EEG-MDD diagnostics. This work bridges computational neuroscience and translational neuroengineering, offering a scalable framework for personalized mental health monitoring.