Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Study on the Correlation between Grip Strength and EEG

악력 세기와 뇌파의 상관관계에 관한 연구

  • Kim, Dong-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 김동은 (중앙대학교 전자전기공학과) ;
  • 박승민 (중앙대학교 전자전기공학과) ;
  • 심귀보 (중앙대학교 전자전기공학과)
  • Received : 2013.05.04
  • Accepted : 2013.07.18
  • Published : 2013.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study was to identify the correlation between electroencephalography (EEG) and strength, using grip strength. 64-channel EEG data were recorded from five healthy subjects in tasks requiring handgrip contractions of nine levels of MVC (Maximal Voluntary Contraction). We found the ERS (Event-Related Synchronization)/ERD (Event-Related Desynchronization) at the measured EEG data using STFT (Short-Time Furier Transform) and spectral power in the EEG of each frequency range displayed in the graph. In this paper, we identified that the stronger we contracted, the greater the spectral power was increased in the ${\beta}$, ${\gamma}$ wave.

Keywords

References

  1. J. N Mak, D. J McFarland, T. M Vaughan, L. M McCane, P. Z Tsui, D. J Zeitlin, E. W Seller, and J. R Wolpow, "EEG correlation of P300-based BCI (Brain-Computer Interface) performance in people with amyotrophic lateral sclerosis," Journal of Neural Engineering, vol. 9, no. 2, 9 pages 026014, Apr. 2012. https://doi.org/10.1088/1741-2560/9/2/026014
  2. K. W. Choi and A. Cichocki, "Control of a wheelchair by motor imagery in real time," Intelligent Data Engineering and Automated Learning-IDEAL, vol. 5326, pp. 330-337, May 2008.
  3. G. Onose, C. Grozea, A. Anghelescu, C. Daia, C. J. Sinescu, A. V. Ciurea, T. Spircu, A. Mirea, I. Andone, A. Spanu, C. Popescu, A.-S. Mihaescu, S. Fazli, M. Danoczy, and F. Popescu, "On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up," Spinal Cord, vol. 50, pp. 599-608, Mar. 2012. https://doi.org/10.1038/sc.2012.14
  4. S. Saeid and J. A. Chamber, EEG Signal Processing, John Wiley & Sons Inc, Chichester, 2007.
  5. J. H. Kang, J. Y. Kim, C. S. Kim, S. O. Song, and Y. S. Choi, Function Test Neurology, Korea medical book publisher, Seoul, 2012.
  6. S. I. Lee, "EEG and left/right directional analysis for brain-computer interface," Pusan National University, thesis, Korea, February 2011.
  7. Y. H. Kim, K. E. Ko, S. M. Park, and K. B. Sim, "Practical use technology for robot control in BCI environment based on Motor Imagery-P300," Journal of Institute of Control, Robotics and System (in Korean), vol. 19, no. 3, pp. 227-232, Mar. 2013. https://doi.org/10.5302/J.ICROS.2013.13.1866
  8. T. Mima, N. Simpkins, T. Oluwatimilehin, and M. Hallett, "Force level modulates human cortical oscillatory activities," Neuroscience Letters, vol. 2075, no. 2, pp. 77-80, 1999.
  9. J. Z. Liu, Q. Yang, B. Yao, R. W. Brown, and G. H. Yue, "Linear correlation between fractal dimension of EEG signal and handgrip force," Biological Cybernetics, vol. 93, no. 2, pp. 131-140, Aug. 2005. https://doi.org/10.1007/s00422-005-0561-3
  10. B. Yao, J. Z. Liu, R. W. Brown, V. Sahgal, and G. H. Yue, "Nonlinear feature of surface EEG showing systematic brain signal adaptations with muscle force and fatigue," Brain Research, 1272, pp. 89-98, May 2009. https://doi.org/10.1016/j.brainres.2009.03.042
  11. V. Siemionow, G. H. Yue, Ranganathan, J. Z. Liu, and V. Sahgal, "Relationship between motor activity-related cortical potential and voluntary muscle activation," Experimental Brain Research, vol. 133, no. 3, pp. 303-311, Aug. 2000. https://doi.org/10.1007/s002210000382
  12. D. E. Kim, T. J. Lee, S. M. Park, K. E. Ko, and K. B. Sim, "EEG analysis following change in hand grip force level for BCI based robot arm force control," Journal of Korean Institute of Intelligent Systems (in Korean), vol. 23, no. 2, pp. 172-177, Apr. 2013. https://doi.org/10.5391/JKIIS.2013.23.2.172
  13. D. C. Lee and Y. J. Choi, "Motion and force estimation system of human fingers," Journal of Institute of Control, Robotics and System (in Korean), vol. 17, no. 10, pp. 1014-1020, Oct. 2011. https://doi.org/10.5302/J.ICROS.2011.17.10.1014
  14. T. J. Lee, S. M. Park, and K. B. Sim, "Brain wave characteristic analysis by multi-stimuli with EEG channel grouping based on binary harmony search," Journal of Institute of Control, Robotics and System (in Korean), vol. 19, no. 8, pp. 725-730, Aug. 2013. https://doi.org/10.5302/J.ICROS.2013.13.1915

Cited by

  1. Evaluation of Hand Grip Strength and EMG Signal on Visual Reaction vol.24, pp.2, 2014, https://doi.org/10.5103/KJSB.2014.24.2.161
  2. EEG Feature Classification Based on Grip Strength for BCI Applications vol.15, pp.4, 2015, https://doi.org/10.5391/IJFIS.2015.15.4.277
  3. Vowel Classification of Imagined Speech in an Electroencephalogram using the Deep Belief Network vol.21, pp.1, 2015, https://doi.org/10.5302/J.ICROS.2015.14.0073
  4. EEG Feature Classification for Precise Motion Control of Artificial Hand vol.25, pp.1, 2015, https://doi.org/10.5391/JKIIS.2015.25.1.029