DOI QR코드

DOI QR Code

Connectivity Analysis Between EEG and EMG Signals by the Status of Movement Intention

운동 의도에 따른 뇌파-근전도 신호 간 연결성 분석

  • Kim, Byeong-Nam (Department of Medical Engineering, College of Medicine, Yonsei University) ;
  • Kim, Yun-Hee (Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular and Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Kim, Laehyun (Center for Bionics, Korea Institute of Science and Technology) ;
  • Kwon, Gyu-Hyun (Graduate School of Technology and Innovation Management, Hanyang University) ;
  • Jang, Won-Seuk (Department of Medical Engineering, College of Medicine, Yonsei University) ;
  • Yoo, Sun-Kook (Department of Medical Engineering, College of Medicine, Yonsei University)
  • 김병남 (연세대학교 의과대학 의학공학교실) ;
  • 김연희 (성균관대학교 의과대학 삼성서울병원 재활의학교실, 심장뇌혈관병원 예방재활센터) ;
  • 김래현 (한국과학기술연구원 바이오닉스연구단) ;
  • 권규현 (한양대학교 기술경영전문대학원) ;
  • 장원석 (연세대학교 의과대학 의학공학교실) ;
  • 유선국 (연세대학교 의과대학 의학공학교실)
  • Received : 2015.08.21
  • Accepted : 2016.01.22
  • Published : 2016.03.31

Abstract

The brain and muscles both of which are composed of top-down structure occur the connectivity with the change of Electroencephalogram(EEG) and Electromyogram(EMG). In this paper, we studied the difference of functional connectivity between brain and muscles that by applying coherence method to EEG and EMG signals when users exercised upper limb with and without the movement intention. The changes in the EEG and EMG signals were inspected using coherence method. During the upper limb exercise, the mu (8~14 Hz) and beta (15~30 Hz) rhythms of the EEG signal at the motor cortex area are activated. And then the beta and piper (30~60 Hz) rhythms of the EMG signal are activated as well. The result of coherence analysis between EEG and EMG showed the coefficient of active exercise including movement intention is significantly higher than passive exercise. The coherence relations between cognitive response and muscle movement could interpret that the connectivity between the brain and muscle appear during active exercise with movement intention. The feature of coherence between brain and muscles by the status of movement intention will be useful in designing the rehabilitation system requiring feedback depending on the users' movement intention status.

뇌와 근육은 상의 하달식 구조로 상지 운동 수행 과정에서 뇌파와 근전도 신호의 변화와 함께 기능적 연결성이 발생한다. 본 논문에서는 사용자가 상지 운동을 수행하였을 때의 뇌파와 근전도 신호에 대해 코히어런스 방법을 적용하여 운동 의도 여부에 따른 뇌와 근육간의 연결성 차이를 규명하고자 한다. 상지 운동을 수행하는 과정에서 운동 피질 영역의 뇌파는 뮤 리듬(mu rhythm, 8~14 Hz)과 베타 리듬(beta rhythm, 15~30 Hz)에서 활성화 되며, 근전도 신호는 베타 리듬과 파이퍼 리듬(piper rhythm, 30~60 Hz)에서 활성화 된다. 뇌파와 근전도 신호간의 코히어런스 분석 결과 운동 의도를 포함한 능동 운동 수행 시 수동 운동을 수행하였을 때 보다 유의미한 차이로 높은 코히어런스 계수가 확인되었다. 이는 인지적 반응과 근육의 움직임 사이의 코히어런스 관계로 운동 의도가 포함된 상지 운동 수행 과정에서의 뇌와 근육간의 연결성을 해석할 수 있었다. 운동 의도에 따른 뇌-근육간의 코히어런스 특징을 이용한다면 재활기기 사용자의 운동 의도에 따라 피드백이 필요한 재활 훈련 시스템 설계에 도움이 될 수 있을 것으로 사료된다.

Keywords

References

  1. Ang, K. K., Guan, C., Sui Geok Chua, K., Ang, B. T., Kuah, C., Wang, C., & Zhang, H. (2010). Clinical study of neurorehabilitation in stroke using EEGbased motor imagery brain-computer interface with robotic feedback. In Engineering in Medicine and Biology Society (EMBC). 2010 Annual International Conference of the IEEE. IEEE. 5549-5552.
  2. Blank, A. A., French, J. A., Pehlivan, A. U., & O'Malley, M. K. (2014). Current trends in robot-assisted upper-limb stroke rehabilitation: promoting patient engagement in therapy. Current Physical Medicine and Rehabilitation Reports, 2(3), 184-195. https://doi.org/10.1007/s40141-014-0056-z
  3. Brown, P. (2000). Cortical drives to human muscle: the Piper and related rhythms. Progress in Neurobiology, 60(1), 97-108. https://doi.org/10.1016/S0301-0082(99)00029-5
  4. Fu, A., Xu, R., He, F., Qi, H., Zhang, L., Ming, D., Bai, Y., & Zhang, Z. (2014). Corticomuscular coherence analysis on the static and dynamic tasks of hand movement. In Digital Signal Processing (DSP), 2014 19th International Conference on, IEEE. 715-718.
  5. Guger, C., Ramoser, H., & Pfurtscheller, G. (2000). Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface(BCI). Rehabilitation Engineering, IEEE Transactions on, 8(4), 447-456. https://doi.org/10.1109/86.895947
  6. Gwin, J. T. & Ferris, D. P. (2012). Beta-and gammarange human lower limb corticomuscular coherence. Frontiers in Human Neuroscience, 6, 258.
  7. Jiang, L., Guan, C., Zhang, H., Wang, C., & Jiang, B. (2011). Brain computer interface based 3D game for attention training and rehabilitation. In Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on. IEEE. 124-127.
  8. Kim Y. J., Whang, M. C., & Woo, J. C. (2008). A Research on Training Effect of EEG according to Repetitive Movement of a Hand. Korean Journal of the Science of Emotion & Sensibility, 11(3), 357-364.
  9. Kleim, J. A. & Jones, T. A. (2008). Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. Journal of Speech, Language, and Hearing Research, 51(1), S225-S239. https://doi.org/10.1044/1092-4388(2008/018)
  10. Kristeva, R., Patino, L., & Omlor, W. (2007). Beta-range cortical motor spectral power and corticomuscular coherence as a mechanism for effective corticospinal interaction during steady-state motor output. Neuroimage, 36(3), 785-792. https://doi.org/10.1016/j.neuroimage.2007.03.025
  11. Meng, F., Tong, K. Y., Chan, S. T., Wong, W. W., Lui, K. H., Tang, K. W., Gao, X., & Gao, S. (2008). Study on connectivity between coherent central rhythm and electromyographic activities. Journal of Neural Engineering, 5(3), 324-332. https://doi.org/10.1088/1741-2560/5/3/005
  12. Mima, T. & Hallett, M. (1999). Corticomuscular coherence:a review. Journal of Clinical Neurophysiology, 16(6), 501-511. https://doi.org/10.1097/00004691-199911000-00002
  13. Nunez, P. L., Silberstein, R. B., Cadusch, P. J., Wijesinghe, R. S., Westdorp, A. F., & Srinivasan, R. (1994). A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging. Electroencephalography and Clinical Neurophysiology, 90(1), 40-57. https://doi.org/10.1016/0013-4694(94)90112-0
  14. Pfurtscheller, G. & Da Silva, F. L. (1999). Event-related EEG/MEG synchronization and desynchronization:basic principles. Clinical Neurophysiology, 110(11), 1842-1857. https://doi.org/10.1016/S1388-2457(99)00141-8