Zhang Wei ^*

• Jilin Justice Officer Academy, Changchun, China

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 symptoms benefits of physical activity in adolescents. Current research methodologies in this domain primarily rely on behavioral and self-reported data, which lack 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 symptoms responses. To address these gaps, we propose an Adaptive Physical Education Optimization Model (APEO) integrated with EEG analysis to monitor and optimize the mental health symptoms 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 captures 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 symptoms outcomes, offering a scalable, data-driven solution to optimize adolescent mental well-being through physical education.