Enhancing Brain Signal Generation Through A Hybrid Approach Integrating Reinforcement Learning And Diffusion Models

Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and interindividual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.