Zhai Zhaofeng, Han Lu, Zhang Wei
School of Education, Qufu Normal University, Qufu, Shandong, China.
School of Physical Education, Jining College, Qufu, Shandong, China.
Front Public Health. 2025 Feb 6;13:1551374. doi: 10.3389/fpubh.2025.1551374. eCollection 2025.
The application of EEG technology in the context of school physical education offers a promising avenue to explore the neural mechanisms underlying the mental health symptom benefits of physical activity in adolescents. Current research methodologies in this domain primarily rely on behavioral and self-reported data, which ack the precision to capture the complex interplay between physical activity and cognitive-emotional outcomes. Traditional approaches often fail to provide real-time, objective insights into individual variations in mental health symptom responses.
To address these gaps, we propose an Adaptive Physical Education Optimization (APEO)model integrated with EEG analysis to monitor and optimize the mental health symptom impacts of physical education programs. APEO combines biomechanical modeling, engagement prediction through recurrent neural networks, and reinforcement learning to tailor physical activity interventions. By incorporating EEG data, our framework captured neural markers of emotional and cognitive states, enabling precise evaluation and personalized adjustments.
Preliminary results indicate that our system enhances both engagement and mental health symptom outcomes, offering a scalable, data-driven solution to optimize adolescent mental wellbeing through physical education.
脑电图（EEG）技术在学校体育教育中的应用为探索体育活动对青少年心理健康症状有益影响的神经机制提供了一条有前景的途径。该领域目前的研究方法主要依赖于行为数据和自我报告数据，缺乏捕捉体育活动与认知 - 情感结果之间复杂相互作用的精确性。传统方法往往无法提供关于心理健康症状反应个体差异的实时、客观见解。
为了弥补这些差距，我们提出了一种与脑电图分析相结合的适应性体育教育优化（APEO）模型，以监测和优化体育教育项目对心理健康症状的影响。APEO结合了生物力学建模、通过循环神经网络进行的参与度预测以及强化学习来定制体育活动干预措施。通过纳入脑电图数据，我们的框架捕捉了情绪和认知状态的神经标志物，从而能够进行精确评估和个性化调整。
初步结果表明，我们的系统提高了参与度和心理健康症状改善效果，为通过体育教育优化青少年心理健康提供了一种可扩展的、数据驱动的解决方案。
