Using EEG technology to enhance performance measurement in physical education
IntroductionThe 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 beha...
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| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1551374/full |
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| author | Zhaofeng Zhai Zhaofeng Zhai Lu Han Wei Zhang |
| author_facet | Zhaofeng Zhai Zhaofeng Zhai Lu Han Wei Zhang |
| author_sort | Zhaofeng Zhai |
| collection | DOAJ |
| description | IntroductionThe 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.MethodsTo 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 discussionPreliminary 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. |
| format | Article |
| id | doaj-art-2c766f94c34046a2912cdc88679e156d |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-2c766f94c34046a2912cdc88679e156d2025-02-06T07:09:28ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-02-011310.3389/fpubh.2025.15513741551374Using EEG technology to enhance performance measurement in physical educationZhaofeng Zhai0Zhaofeng Zhai1Lu Han2Wei Zhang3School of Education, Qufu Normal University, Qufu, Shandong, ChinaSchool of Physical Education, Jining College, Qufu, Shandong, ChinaForeign Languages Department, Jining Vocational and Technical College, Jining, Shandong, ChinaJilin Justice Officer Academy, Changchun, ChinaIntroductionThe 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.MethodsTo 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 discussionPreliminary 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1551374/fullEEG analysisphysical educationadolescent mental health symptomsneural mechanismsengagement optimization |
| spellingShingle | Zhaofeng Zhai Zhaofeng Zhai Lu Han Wei Zhang Using EEG technology to enhance performance measurement in physical education Frontiers in Public Health EEG analysis physical education adolescent mental health symptoms neural mechanisms engagement optimization |
| title | Using EEG technology to enhance performance measurement in physical education |
| title_full | Using EEG technology to enhance performance measurement in physical education |
| title_fullStr | Using EEG technology to enhance performance measurement in physical education |
| title_full_unstemmed | Using EEG technology to enhance performance measurement in physical education |
| title_short | Using EEG technology to enhance performance measurement in physical education |
| title_sort | using eeg technology to enhance performance measurement in physical education |
| topic | EEG analysis physical education adolescent mental health symptoms neural mechanisms engagement optimization |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1551374/full |
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