# Using EEG technology to enhance performance measurement in physical education

**Authors:** Zhaofeng Zhai, Lu Han, Wei Zhang

PMC · DOI: 10.3389/fpubh.2025.1551374 · Frontiers in Public Health · 2025-02-06

## TL;DR

This paper explores using EEG technology in school physical education to better understand and improve mental health benefits through personalized activity adjustments.

## Contribution

The novel APEO model integrates EEG data with biomechanical modeling and machine learning to optimize mental health outcomes in physical education.

## Key findings

- The APEO model improves engagement and mental health symptom outcomes in adolescents.
- EEG data enables real-time monitoring of emotional and cognitive states during physical activity.
- The system offers a scalable, data-driven approach to personalize physical education interventions.

## Abstract

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.

## Full-text entities

- **Diseases:** mental health symptom (OMIM:603663)

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11839608/full.md

## References

46 references — full list in the complete paper: https://tomesphere.com/paper/PMC11839608/full.md

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Source: https://tomesphere.com/paper/PMC11839608