Using EEG technology to enhance performance measurement in physical education.

Introduction: 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. Methods: 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. Results and discussion: 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.