Browse > Article
http://dx.doi.org/10.14695/KJSOS.2016.19.1.31

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

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)
Publication Information
Science of Emotion and Sensibility / v.19, no.1, 2016 , pp. 31-38 More about this Journal
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.
Keywords
Electroencephalogram(EEG); Electromyogram(EMG); Movement Intention; Coherence; Connectivity;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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.   DOI
3 Brown, P. (2000). Cortical drives to human muscle: the Piper and related rhythms. Progress in Neurobiology, 60(1), 97-108.   DOI
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.   DOI
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 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.   DOI
10 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.   DOI
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.   DOI
12 Mima, T. & Hallett, M. (1999). Corticomuscular coherence:a review. Journal of Clinical Neurophysiology, 16(6), 501-511.   DOI
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.   DOI
14 Pfurtscheller, G. & Da Silva, F. L. (1999). Event-related EEG/MEG synchronization and desynchronization:basic principles. Clinical Neurophysiology, 110(11), 1842-1857.   DOI