EEG-PIEDA: Reinforcement Learning for EEG-Driven Personalized Video Intervention with Emotional Dynamics Awareness

Achieving personalized and adaptive mental stress regulation in dynamic environments remains challenging due to substantial variations in individual emotional responses and the temporal dependency of affective states. Although video-based interventions provide an accessible approach for stress alleviation, existing methods mainly rely on behavioral feedback and cannot characterize individual neural responses or the stability of emotional regulation processes.To ad-dress these challenges, we propose EEG-PIEDA, an EEG-guided personalized video regulation framework that integrates neural response pattern modeling, continuous emotion perception, and reinforcement learning. First, we analyze dynamic EEG differential entropy and functional connectivity variations to discover individual emotional response patterns during video stimulation. Then, we introduce a pattern-aware dual-branch affective perception network that adaptively fuses instantaneous EEG representations with individual response characteristics for continuous emotional state estimation. Furthermore, we formulate emotional regulation as a reinforcement learning problem and model emotional state transitions under different perturbation intensities to quantify individual emotional anti-interference capability. Based on the learned policy, EEG-PIEDA enables personalized video selection according to users’ neural response dynamics and regulatory characteristics. Experiments on real-world EEG datasets demonstrate that our framework accurately identifies heterogeneous emotional response patterns, effectively captures dynamic emotional evolution, and achieves superior personalized stress regulation performance compared with existing approaches.