ХАРАКТЕРИСТИКИ БИОЭЛЕКТРИЧЕСКОЙ АКТИВНОСТИ ГОЛОВНОГО МОЗГА ПРИ ТРЕНИРОВКЕ С ИСПОЛЬЗОВАНИЕМ АППАРАТОВ С ФУНКЦИЕЙ ОБРАТНОЙ СВЯЗИ
Ключевые слова:
биологическая обратная связь, координация, стабилометрия, спортивная тренировка, электроэнцефалография
Аннотация
Цель: изучить влияние тренингов с использованием аппаратов с функцией обратной связи на характеристики биоэлектрической активности головного мозга. Материалы и методы. В исследовании приняли участие 60 девушек в возрасте 18–20 лет, не занимающихся спортом. Проводились спортивные тренировки на развитие вестибулярного анализатора и проприоцептивной чувствительности; а также на тренажерах с использованием обратной связи по параметру «положение проекции центра тяжести» и по параметру «прилагаемые усилия». Запись ЭЭГ осуществлялась при проведении проб Ромберга и Бирюк до и после курса тренировок. Результаты. Показано, что специфика физиологических механизмов различных видов тренировок, направленных на развитие вестибулярного анализатора и проприоцептивной чувствительности, находит свое отражение в формировании специфических паттернов биоэлектрической активности коры головного мозга. Традиционные способы тренировки слабее всего отражаются на характеристиках ЭЭГ, в то время как тренировки с БОС характеризуются более выраженным влиянием. Наряду со сходными эффектами (усиление медленной активности тета- и дельта-диапазона преимущественно во фронтальной области коры и активация высокочастотной бета-активности в затылочных отведениях) регистрируются и различия. Если тренировка с использованием в качестве канала БОС параметра «положение проекции центра тяжести» сопровождалась угнетением активности альфа-диапазона, то тренировка с использованием в качестве канала БОС параметра «прилагаемые усилия», напротив, способствовала росту средней мощности альфа-активности в затылочной области коры. Заключение. Параметры биоэлектрической активности коры головного мозга являются информативными при проведении тренировок с БОС и могут использоваться для контроля их эффективности и оценки физиологических аспектов.
Литература
1. Попадюха, Ю.А. Перспективы использования компьютерных систем Huber в оздоровлении, профилактике повреждений и физической реабилитации / Ю.А. Попадюха, Г.В. Коробейников // Педагогика, психология и медико-биологические проблемы физического воспитания и спорта. – 2012. – № 1. – C. 88–93.
2. Слива, А.С. Стабилографический тренажер / А.С. Слива, Г.Ю. Джуплина // Известия ЮФУ. Технические науки. – 2009. – № 9. – С. 242–244.
3. Barbado, D. How much trunk control is affected in adults with moderate-to-severe cerebral palsy? / D. Barbado, R. Reina, A. Roldan et al. // Journal of Biomechanics. – 2019. – Vol. 82. – P. 368–374.
4. Cheron, G. Brain oscillations in sport: Toward EEG biomarkers of performance / G. Cheron, G. Petit, J. Cheron et al. // Frontiers in Psychology. – 2016. – Vol. 7 (FEB).
5. Düking, P. Necessary steps to accelerate the integration of wearable sensors into recreation and competitive sports / P. Düking, C. Stammel, B. Sperlich et al. // Current Sports Medicine Reports. – 2018. – Vol. 17 (6). – P. 178–182.
6. Düking, P. The potential usefulness of virtual reality systems for athletes: A short SWOT analysis / P. Düking, H.-C. Holmberg, B. Sperlich // Frontiers in Physiology. – 2018. – Vol. 9 (MAR). – P. 128.
7. Ericksen, H.M. Immediate effects of real-time feedback on jump-landing kinematics / H.M. Ericksen, A.C. Thomas, P.A. Gribble et al. // Journal of Orthopaedic and Sports Physical Therapy. – 2015. – Vol. 45 (2). – P. 112–118.
8. Gong, A. Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm / A. Gong, J. Liu, L. Lua et al. // Biomedical Signal Processing and Control. – 2019. – Vol. 51. – P. 128–137.
9. Illarionova, A.V. Distinctive features of intramuscular and intermuscular coordination at power graduation in the context of balance training / A.V. Illarionova, L.V. Kapilevich // Teoriya i Praktika Fizicheskoy Kultury. – 2014. – Vol. 12. – P. 44–46.
10. Ji, L. Correlation analysis of EEG alpha rhythm is related to golf putting performance / L. Ji, H. Wang, T.Q. Zheng et al. // Biomedical Signal Processing and Control. – 2019. – Vol. 49. – P. 124–136.
11. Jo, E. Validation of biofeedback wearables for photoplethysmographic heart rate tracking / E. Jo, K. Lewis, D. Directo et al. // Journal of Sports Science and Medicine. – 2016. – Vol. 15 (3). – P. 540–547.
12. Kapilevich, L.V. Physiological basis of the improvement of movement accuracy on the basis of stabilographic training with biological feedback / L.V. Kapilevich, E.V. Koshelskaya, S.G. Krivoschekov // Human Physiology. – 2015. – Vol. 41 (4). – P. 404–411.
13. Kapilevich, L.V. Physiological mechanisms to ensure accuracy and coordination of movements under conditions of unstable equilibrium and moving target (the case of strikes in sports karate) / L.V. Kapilevich, F.A. Guzhov, Yu. P.Bredikhina, A.A. Il'in // Teoriya i Praktika Fizicheskoy Kultury. – 2014. – Vol. 12. – С. 22–24.
14. Kos, A. Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications / A. Kos, V. Milutinović, A. Umek // Future Generation Computer Systems. – 2019. – Vol. 92. – P. 582–592.
15. Park, J.L. The case for mobile cognition: EEG and sports performance / J.L. Park, M.M. Fairweather, D.I. Donaldson // Neuroscience and Biobehavioral Reviews. – 2015. – Vol. 52. – P. 117–130.
16. Peake, J.M. A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations / J.M. Peake, G. Kerr, J.P. Sullivan // Frontiers in Physiology. – 2018. – Vol. 9 (JUN). – P. 743.
17. Zhang, X. Wearables, biomechanical feedback, and human motor-skills' learning & optimization / X. Zhang, G. Shan, Y. Wang et al. // Applied Sciences (Switzerland). – 2019. – Vol. 9 (2). – P. 226.
2. Sliva A.S., Dzhuplina G.Yu. [Stabilization Simulator]. Izvestiya YUFU. Tekhnicheskiye nauki [News of SFU. Technical Science], 2009, no. 9, pp. 242–244. (in Russ.)
3. Barbado D., Reina R., Roldan A. et al. How Much Trunk Control is Affected in Adults with Moderate-to-Severe Cerebral Palsy? Journal of Biomechanics, 2019, vol. 82, pp. 368–374. DOI: 10.1016/j.jbiomech.2018.11.009
4. Cheron G., Petit G., Cheron J. et al. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance. Frontiers in Psychology, 2016, vol. 7. DOI: 10.3389/fpsyg.2016.00246
5. Düking P., Stammel C., Sperlich B. et al. Necessary Steps to Accelerate the Integration of Wearable Sensors Into Recreation and Competitive Sports. Current Sports Medicine Reports, 2018, vol. 17 (6), pp. 178–182. DOI: 10.1249/JSR.0000000000000495
6. Düking P., Holmberg H.C., Sperlich B. The Potential Usefulness of Virtual Reality Systems for Athletes: A Short SWOT Analysis. Frontiers in Physiology, 2018, vol. 9, 128 p. DOI: 10.3389/fphys.2018.00128
7. Ericksen H.M., Thomas A.C., Gribble P.A. et al. Immediate Effects of Real-Time Feedback on Jump-Landing Kinematics. Journal of Orthopaedic and Sports Physical Therapy, 2015, vol. 45 (2), pp. 112–118. DOI: 10.2519/jospt.2015.4997
8. Gong A., Liu J., Lua L. et al. Characteristic Differences Between the Brain Networks of High-Level Shooting Athletes and Non-Athletes Calculated Using the Phase-Locking Value Algorithm. Biomedical Signal Processing and Control, 2019, vol. 51, pp. 128–137. DOI: 10.1016/j.bspc.2019.02.009
9. Illarionova A.V., Kapilevich L.V. Distinctive Features of Intramuscular and Intermuscular Coordination at Power Graduation in the Context of Balance Training. Theory and Practice of Physical Culture, 2014, vol. 12, pp. 44–46.
10. Ji L., Wang H., Zheng T.Q. et al. Correlation Analysis of EEG Alpha Rhythm is Related to Golf Putting Performance. Biomedical Signal Processing and Control, 2019, vol. 49, pp. 124–136. DOI: 10.1016/j.bspc.2018.11.009
11. Jo E., Lewis K., Directo D. et al. Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking. Journal of Sports Science and Medicine, 2016, vol. 15 (3), pp. 540–547.
12. Kapilevich L.V., Koshelskaya E.V., Krivoschekov S.G. Physiological Basis of the Improvement of Movement Accuracy on the Basis of Stabilographic Training with Biological Feedback. Human Physiology, 2015, vol. 41 (4), pp. 404–411. DOI: 10.1134/S036211971504009X
13. Kapilevich L.V., Guzhov F.A., Bredikhina Yu.P., Il'in A.A. Physiological Mechanisms to Ensure Accuracy and Coordination of Movements Under Conditions of Unstable Equilibrium and Moving Target (the Case of Strikes in Sports Karate). Theory and Practice of Physical Culture, 2014, vol. 12, pp. 22–24.
14. Kos A., Milutinović V., Umek A. Challenges in Wireless Communication for Connected Sensors and Wearable Devices Used in Sport Biofeedback Applications. Future Generation Computer Systems, 2019, vol. 92, pp. 582–592. DOI: 10.1016/j.future.2018.03.032
15. Park J.L., Fairweather M.M., Donaldson D.I. The Case for Mobile Cognition: EEG and Sports Performance. Neuroscience and Biobehavioral Reviews, 2015, vol. 52, pp. 117–130. DOI: 10.1016/j.neubiorev.2015.02.014
16. Peake J.M., Kerr G., Sullivan J.P. A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Frontiers in Physiology, 2018, vol. 9, p. 743. DOI: 10.3389/fphys.2018.00743
17. Zhang X., Shan G., Wang Y. et al. Wearables, Biomechanical Feedback, and Human Mo-tor-Skills' Learning & Optimization. Applied Sciences (Switzerland), 2019, vol. 9 (2), p. 226. DOI: 10.3390/app9020226
2. Слива, А.С. Стабилографический тренажер / А.С. Слива, Г.Ю. Джуплина // Известия ЮФУ. Технические науки. – 2009. – № 9. – С. 242–244.
3. Barbado, D. How much trunk control is affected in adults with moderate-to-severe cerebral palsy? / D. Barbado, R. Reina, A. Roldan et al. // Journal of Biomechanics. – 2019. – Vol. 82. – P. 368–374.
4. Cheron, G. Brain oscillations in sport: Toward EEG biomarkers of performance / G. Cheron, G. Petit, J. Cheron et al. // Frontiers in Psychology. – 2016. – Vol. 7 (FEB).
5. Düking, P. Necessary steps to accelerate the integration of wearable sensors into recreation and competitive sports / P. Düking, C. Stammel, B. Sperlich et al. // Current Sports Medicine Reports. – 2018. – Vol. 17 (6). – P. 178–182.
6. Düking, P. The potential usefulness of virtual reality systems for athletes: A short SWOT analysis / P. Düking, H.-C. Holmberg, B. Sperlich // Frontiers in Physiology. – 2018. – Vol. 9 (MAR). – P. 128.
7. Ericksen, H.M. Immediate effects of real-time feedback on jump-landing kinematics / H.M. Ericksen, A.C. Thomas, P.A. Gribble et al. // Journal of Orthopaedic and Sports Physical Therapy. – 2015. – Vol. 45 (2). – P. 112–118.
8. Gong, A. Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm / A. Gong, J. Liu, L. Lua et al. // Biomedical Signal Processing and Control. – 2019. – Vol. 51. – P. 128–137.
9. Illarionova, A.V. Distinctive features of intramuscular and intermuscular coordination at power graduation in the context of balance training / A.V. Illarionova, L.V. Kapilevich // Teoriya i Praktika Fizicheskoy Kultury. – 2014. – Vol. 12. – P. 44–46.
10. Ji, L. Correlation analysis of EEG alpha rhythm is related to golf putting performance / L. Ji, H. Wang, T.Q. Zheng et al. // Biomedical Signal Processing and Control. – 2019. – Vol. 49. – P. 124–136.
11. Jo, E. Validation of biofeedback wearables for photoplethysmographic heart rate tracking / E. Jo, K. Lewis, D. Directo et al. // Journal of Sports Science and Medicine. – 2016. – Vol. 15 (3). – P. 540–547.
12. Kapilevich, L.V. Physiological basis of the improvement of movement accuracy on the basis of stabilographic training with biological feedback / L.V. Kapilevich, E.V. Koshelskaya, S.G. Krivoschekov // Human Physiology. – 2015. – Vol. 41 (4). – P. 404–411.
13. Kapilevich, L.V. Physiological mechanisms to ensure accuracy and coordination of movements under conditions of unstable equilibrium and moving target (the case of strikes in sports karate) / L.V. Kapilevich, F.A. Guzhov, Yu. P.Bredikhina, A.A. Il'in // Teoriya i Praktika Fizicheskoy Kultury. – 2014. – Vol. 12. – С. 22–24.
14. Kos, A. Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications / A. Kos, V. Milutinović, A. Umek // Future Generation Computer Systems. – 2019. – Vol. 92. – P. 582–592.
15. Park, J.L. The case for mobile cognition: EEG and sports performance / J.L. Park, M.M. Fairweather, D.I. Donaldson // Neuroscience and Biobehavioral Reviews. – 2015. – Vol. 52. – P. 117–130.
16. Peake, J.M. A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations / J.M. Peake, G. Kerr, J.P. Sullivan // Frontiers in Physiology. – 2018. – Vol. 9 (JUN). – P. 743.
17. Zhang, X. Wearables, biomechanical feedback, and human motor-skills' learning & optimization / X. Zhang, G. Shan, Y. Wang et al. // Applied Sciences (Switzerland). – 2019. – Vol. 9 (2). – P. 226.
References
1. Popadyukha Yu.A., Korobeynikov G.V. [Prospects for the Use of Computer Systems Huber in the Rehabilitation, Prevention of Injuries and Physical Rehabilitation]. Pedagogika, psikhologiya i mediko-biologicheskiye problemy fizicheskogo vospitaniya i sporta [Pedagogy, Psychology and Biomedical Problems of Physical Education and Sport], 2012, no. 1, pp. 88–93. (in Russ.)2. Sliva A.S., Dzhuplina G.Yu. [Stabilization Simulator]. Izvestiya YUFU. Tekhnicheskiye nauki [News of SFU. Technical Science], 2009, no. 9, pp. 242–244. (in Russ.)
3. Barbado D., Reina R., Roldan A. et al. How Much Trunk Control is Affected in Adults with Moderate-to-Severe Cerebral Palsy? Journal of Biomechanics, 2019, vol. 82, pp. 368–374. DOI: 10.1016/j.jbiomech.2018.11.009
4. Cheron G., Petit G., Cheron J. et al. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance. Frontiers in Psychology, 2016, vol. 7. DOI: 10.3389/fpsyg.2016.00246
5. Düking P., Stammel C., Sperlich B. et al. Necessary Steps to Accelerate the Integration of Wearable Sensors Into Recreation and Competitive Sports. Current Sports Medicine Reports, 2018, vol. 17 (6), pp. 178–182. DOI: 10.1249/JSR.0000000000000495
6. Düking P., Holmberg H.C., Sperlich B. The Potential Usefulness of Virtual Reality Systems for Athletes: A Short SWOT Analysis. Frontiers in Physiology, 2018, vol. 9, 128 p. DOI: 10.3389/fphys.2018.00128
7. Ericksen H.M., Thomas A.C., Gribble P.A. et al. Immediate Effects of Real-Time Feedback on Jump-Landing Kinematics. Journal of Orthopaedic and Sports Physical Therapy, 2015, vol. 45 (2), pp. 112–118. DOI: 10.2519/jospt.2015.4997
8. Gong A., Liu J., Lua L. et al. Characteristic Differences Between the Brain Networks of High-Level Shooting Athletes and Non-Athletes Calculated Using the Phase-Locking Value Algorithm. Biomedical Signal Processing and Control, 2019, vol. 51, pp. 128–137. DOI: 10.1016/j.bspc.2019.02.009
9. Illarionova A.V., Kapilevich L.V. Distinctive Features of Intramuscular and Intermuscular Coordination at Power Graduation in the Context of Balance Training. Theory and Practice of Physical Culture, 2014, vol. 12, pp. 44–46.
10. Ji L., Wang H., Zheng T.Q. et al. Correlation Analysis of EEG Alpha Rhythm is Related to Golf Putting Performance. Biomedical Signal Processing and Control, 2019, vol. 49, pp. 124–136. DOI: 10.1016/j.bspc.2018.11.009
11. Jo E., Lewis K., Directo D. et al. Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking. Journal of Sports Science and Medicine, 2016, vol. 15 (3), pp. 540–547.
12. Kapilevich L.V., Koshelskaya E.V., Krivoschekov S.G. Physiological Basis of the Improvement of Movement Accuracy on the Basis of Stabilographic Training with Biological Feedback. Human Physiology, 2015, vol. 41 (4), pp. 404–411. DOI: 10.1134/S036211971504009X
13. Kapilevich L.V., Guzhov F.A., Bredikhina Yu.P., Il'in A.A. Physiological Mechanisms to Ensure Accuracy and Coordination of Movements Under Conditions of Unstable Equilibrium and Moving Target (the Case of Strikes in Sports Karate). Theory and Practice of Physical Culture, 2014, vol. 12, pp. 22–24.
14. Kos A., Milutinović V., Umek A. Challenges in Wireless Communication for Connected Sensors and Wearable Devices Used in Sport Biofeedback Applications. Future Generation Computer Systems, 2019, vol. 92, pp. 582–592. DOI: 10.1016/j.future.2018.03.032
15. Park J.L., Fairweather M.M., Donaldson D.I. The Case for Mobile Cognition: EEG and Sports Performance. Neuroscience and Biobehavioral Reviews, 2015, vol. 52, pp. 117–130. DOI: 10.1016/j.neubiorev.2015.02.014
16. Peake J.M., Kerr G., Sullivan J.P. A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Frontiers in Physiology, 2018, vol. 9, p. 743. DOI: 10.3389/fphys.2018.00743
17. Zhang X., Shan G., Wang Y. et al. Wearables, Biomechanical Feedback, and Human Mo-tor-Skills' Learning & Optimization. Applied Sciences (Switzerland), 2019, vol. 9 (2), p. 226. DOI: 10.3390/app9020226
Опубликован
2019-08-15
Как цитировать
Илларионова, А., & Капилевич, Л. (2019). ХАРАКТЕРИСТИКИ БИОЭЛЕКТРИЧЕСКОЙ АКТИВНОСТИ ГОЛОВНОГО МОЗГА ПРИ ТРЕНИРОВКЕ С ИСПОЛЬЗОВАНИЕМ АППАРАТОВ С ФУНКЦИЕЙ ОБРАТНОЙ СВЯЗИ. Человек. Спорт. Медицина, 19(S1), 7-17. https://doi.org/10.14529/hsm19s101
Выпуск
Раздел
Физиология
Copyright (c) 2019 Человек. Спорт. Медицина
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