Korean Journal of Applied Biomechanics (한국운동역학회지)

Korean Society of Applied Biomechanics (한국운동역학회)
권호 표지
DOI QR코드

DOI QR Code

Exploration of Motion Prediction between Electroencephalography and Biomechanical Variables during Upright Standing Posture

바로서기 동작 시 EEG와 역학변인 간 동작 예측의 탐구

  • Received : 2024.04.16
  • Accepted : 2024.06.04
  • Published : 2024.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (fCoP, fAJtorque and fEEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using fEEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A2A01047204).

References

  1. Brummer, V., Schneider, S., Abel, T., Vogt, T. & Struder, H. K. (2011). Brain cortical activity is influenced by exercise mode and intensity. Medicine & Science in Sports & Exercise by the American College of Sports Medicine, 43(10), 1863-1872.
  2. Chen, X., Tao, X., Wang, F. L. & Xie, H. (2022). Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Computing and Applications, 34, 11295-11333. https://doi.org/10.1007/s00521-020-05588-x
  3. Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. (2012). Neural population dynamics during reaching. Nature, 5; 487(7405), 51-56. https://doi.org/10.1038/nature11129
  4. Eun, S. J., Kim, J. Y. & Kim, K. H. (2021). Applications of artificial intelligence in urological setting: a hopeful path to improved care. Journal of Exercise Rehabilitation, 17(5), 308-312. https://doi.org/10.12965/jer.2142596.298
  5. Evarts, E. V. (1968). Relation of pyramidal tract activity to force exerted during voluntary movement. Journal of Neurophysiology, 31(1), 14-27. https://doi.org/10.1152/jn.1968.31.1.14
  6. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics: And Sex and Drugs and Rock "N" Roll, 4th Edit., Sage, Los Angeles, London, New Delhi.
  7. Gemein, L. A. W., Schirrmeister, R. T., Chrabaszcz, P., Wilson, D., Boedecker, J., Andreas, S. B., Hutter, F. & Ball, T. (2020). Machine-learning-based diagnostics of EEG pathology. NeuroImage, 15; 220.
  8. Hamet, P. & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40. https://doi.org/10.1016/j.metabol.2017.01.011
  9. Hall, S. J. (2016). Basic Biomechanics. 6th edit. The McGraw-Hill Companies. NewYork: Mosby Incorporated.
  10. Hong, S. J. (2018). Analysis of brain connectivity in gait cycle using EEG during walking on the treadmill. Un-published Master's Thesis. KAIST. Dept. of Mechanical Engineering.
  11. Jin, S. H., Ahn, J. U., Lee, S. H., Jang, G. H. & Lee, Y. J. (2014). Study on the Correlation between Brain Signal and Muscle Signal during Treadmill Walking. Korean Society of Precision Engineers, Spring Conference Proceedings, 14S895. Daegu Gyeongbuk Institute of Science and Technology (DGIST), Robotics System Research Department.
  12. Kim, D. Y., Lee, J. H., Park, M. H., Choi, Y. H. & Park Y. O. (2017). Trends in Brain Wave Signal and Application Technology. Electronics and Telecommunications Trends. ETRI, 32(2), 19-28.
  13. Kim, E. S. & Oh, C. S. (1997). Artificial Intelligence: Analysis of Statistical Neurodynamics for the Effects of the Hysteretic Property on the Performance of Sequential Associative Neural Nets. The KIPS Transactions, 4(4), 1035-1045.
  14. Kim, J. H., Yuk, G. C. & Bae, S. S. (2003). Neurobiology and Neurobiomechanics for Neural Mobilization. Journal of Korean Physical Therapy, 15(2), 225-230.
  15. Kim, S. P. (2019). Development of neural information processing technology for somatosensory integrated real-time movement control. Ulsan National Institute of Science and Technology. Ministry of Science and ICT. 2016M3C7A1904988.
  16. Kouvelioti, V., Kellis, E., Kofotolis, N. & Amiridis, I. (2015). Reliability of Single-leg and Double-leg Balance Tests in Subjects with Anterior Cruciate Ligament Reconstruction and Controls. Research in Sports Medicine an International Journal, 23(2), 1-16, DOI:10.1080/15438627.2015.1005292
  17. Latash, M. L. & Zatsiorsky, V. M. (2016). Biomechanics and Motor Control. Elsevier Inc.
  18. Lemon, R. N. (2008). An enduring map of the motor cortex. Experimental Physiology, 93, 798-802. https://doi.org/10.1113/expphysiol.2007.039081
  19. Lee, H. S. (2022). The Role of Exercise Science in Human Health Digital Technology Era. Exercise Science, 31(1), 1-3. https://doi.org/10.15857/ksep.2021.00654
  20. Mao, S. & Vinson, V. (2018). Power couple: Science and technology. Science, 361(6405), 864-865. DOI:10.1126/science. 361.6405.864.
  21. O'Reilly, R. C. & Munakata, Y. (2000). Computational explorations in cognitive neuroscience, Understanding the Mind by Simulating the Brain; The MIT Press, 2000; 1-532.
  22. Park, S. K. (2006). Quantification of human postural balancing performance. Journal of the Korean Society for Precision Engineering, 23(2), 21-26. 2287-8769(eISSN).
  23. Presacco, A., Forrester, L. W. & Contreras-Vidal, J. L. (2012). Decoding intra-limb and inter-limb kinematics during treadmill walking from scalp electroencephalographic (EEG) signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(2), 212-219. https://doi.org/10.1109/TNSRE.2012.2188304
  24. Ratey, J. J. & Hagerman, E. S. (2008) Spark: The revolutionary New Science of Exercise and the Brain. Little, Brown Spark Company. 4-304.
  25. Siddiqi, F. A., Masood, T., Osama, M., Azim, M. E. & Babur, M. N. (2019). Common balance measures and fall risk scores among older adults in Pakistan: Normative values and correlation. Journal of the Pakistan Medical Association, 69(2), 246-249. PMID: 30804593.
  26. Slobounov, S., Hallett, M., Caoa, C. & Newell, K. (2006). Modulation of cortical activity as a result of voluntary postural sway direction: An EEG study. Neuroscience Letters, 442, 309-313. https://doi.org/10.1016/j.neulet.2008.07.021
  27. Song, J., Ampatzis, K., Bjornfors, E. R. & Manira, A. E. (2016). Motorneurons control locomotor circuit function retrogradely via gap junctions. Nature, 529, 399-402. https://doi.org/10.1038/nature16497
  28. Stehle, S. A., Aubonnet, R., Hassan, M., Recenti, M., Jacob, D., Petersen, H. & Gargiulo, P. (2022). Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes: BioVRSea paradigm. Fronttiers in Human Neuroscience, 15; 16, 1038976.
  29. Tse, Y. Y., Petrofsky, J. S., Berk, L., Daher, N., Lohman, E., Laymon, M. S. & Cavalcanti, P. (2013). Postural sway and rhythmic electroencephalography analysis of cortical activation during eight balance training tasks. Medical Science Monitor, 8; 19, 175-186.
  30. Vyas, S., Golub, M. D., Sussillo, D. & Shenoy, K. V. (2020). Computation Through Neural Population Dynamics. Annual Review Neuroscience, 8(43), 249-275.
  31. Williams, C. (2022). Move: How the New Science of Body Movement Can Set Your Mind Free. Hanover Square Press.
  32. Yoo, K. S. (2019). Studies in biomechanical properties on brain-spinal cord response mechanism by human posture control ability. The Korean Journal of Physical Education 58(6), 449-459. pISSN: 1738-964X, eISSN: 2508-7029 https://doi.org/10.23949/kjpe.2019.07.58.6.36
  33. Yoo, K. S. (2020). A study on bio-based Motion Command Signal for stability in quiet standing posture. KSSS, 29(1), 783-792.