An EEG-Based Positive Feedback Mechanism for VR Mindfulness Meditation to Improve Emotion Regulation
Qing Wang, Fang Liu, Baorong Yang, Zheyuan Yang, Jingyang Huang, Yuxin Xu, Chengcheng Zheng, Yingying She, Hanshu Cai, Fuze Tian
IEEE Transactions on Computational Social Systems October 1, 2025 DOI: 10.1109/tcss.2025.3557711 (opens in new tab) via Semantic Scholar
Summary
AI-generated from the abstractAn EEG-based positive feedback mechanism for VR mindfulness meditation improves emotion regulation more than meditation without feedback. The system computes emotional state from EEG, assesses relaxation progress, and adapts feedback in real time. In a randomized controlled trial with 36 participants, both physiological measures and self-reported relaxation increased significantly with the feedback mechanism compared to interventions without feedback. The findings suggest that personalized, adaptive feedback can enhance engagement and effectiveness in digital mental health interventions.
Study at a glance
| Characteristics | Randomized controlled trial Peer reviewed |
|---|---|
| Sample size | 36 |
| Intervention | EEG-based positive feedback mechanism for VR mindfulness meditation |
| Keywords | Computer science Psychology |
| Key finding | An EEG-based positive feedback mechanism for VR mindfulness meditation significantly increases physiological measures and self-reported relaxation compared to interventions without feedback. |
Abstract
Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of emotion regulation. This mechanism consists of three modules: 1) EEG-based emotional state computation; 2) process-based relaxation assessment; and 3) adaptive positive decision feedback. Collectively, these components form a computation-assessment-feedback closed-loop system that objectively quantifies emotions while enabling real-time decision adjustments based on emotional trends, thereby enhancing user engagement and emotion regulation efficacy through personalized feedback. The contribution of the proposed feedback mechanism was evaluated through a randomized controlled trial (N = 36). The results indicated that both physiological measures and self-reported relaxation significantly increased when compared to interventions without feedback. These findings validate that the EEG-based positive feedback mechanism effectively enhances emotion regulation while providing additional insights into improving both the engagement and effectiveness within digital mental health interventions.