2026-05-25 00:00 China United Kingdom Papers Foundations & Methods Translated from EN

Enhancing Brain Signal Generation Through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models

Summary The study introduces RLED, a reinforcement learning-enhanced EEG diffusion framework for adaptive data augmentation in endogenous EEG tasks such as motor imagery and emotion recognition. Reinforcement learning dynamically regulates the diffusion training process to balance temporal, spectral and category-related features. Across four datasets, the high-quality synthetic EEG signals it generated consistently improved classification performance.

Why it matters Synthetic EEG that reliably lifts classification attacks the data-collection bottleneck — subject fatigue and inter-subject variability — that caps how much real training data BCI developers can gather.

This study, published in IEEE Transactions on Medical Imaging, proposes RLED, a reinforcement learning-enhanced EEG diffusion framework for adaptive data augmentation in endogenous EEG tasks, focusing on motor imagery and emotion recognition, with a reinforcement learning mechanism that dynamically regulates the diffusion training process to balance temporal, spectral and class-related features.

Experiments on four datasets demonstrate that the method generates high-quality synthetic EEG signals and consistently improves classification performance; the authors say RLED may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications. PMID 42184181, DOI 10.1109/tmi.2026.3696676

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pubmed.ncbi.nlm.nih.gov 2026-05-25

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