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http://dx.doi.org/10.5391/JKIIS.2015.25.1.029

EEG Feature Classification for Precise Motion Control of Artificial Hand  

Kim, Dong-Eun (Department of Electrical and Electronics Engineering, Chung-Ang University)
Yu, Je-Hun (Department of Electrical and Electronics Engineering, Chung-Ang University)
Sim, Kwee-Bo (Department of Electrical and Electronics Engineering, Chung-Ang University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.25, no.1, 2015 , pp. 29-34 More about this Journal
Abstract
Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.
Keywords
BCI; EEG; Common spatial pattern; Artificial arm control;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 V. Srinivasan, C. Eswaran, and N. Sriraam, "Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks," IEEE Inforamtion Technology in Biomedicine, vol. 11, no. 3, pp. 288-295, May 2007.   DOI
2 G. E. Frye, C. K. Hauser, G. Townsend, and E. W. Sellers, "Suppressing flashes of items surrounding targets during calibration of a P300-based brain- computer interface improves performance." Journal of neural engineering, vol. 8, no. 2, 025024, March 2011.   DOI
3 J. del R. Millan, F. Galan, D. Vanhooydonck, E. Lew, J. Philips, and M. Nuttin, "Asynchronous Non-Invasive Brain-Actuated Control of an Intelligent Wheelchair," IEEE Int.Conf Engineering In Medicine And Biology Society, pp. 3361-3364, September 2009.
4 B. Rebsamen, C. Guan, H. Zhang, C. Wang, C. Teo, M. H. Ang, and E. Burdet, "A brain controlled wheelchair to navigate in familiar environments," IEEE Neural Systems and Rehabilitation Engineering, vol. 18, no. 6, pp. 590-598, December 2010.   DOI
5 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.   DOI   ScienceOn
6 R. Kristeva, L. Patino, and W. Omlor, "Beta-range cortical motor spectral power and corticomuscular coherence as a mechanism for effective corticospinal interaction during steady-state motor output," Neuroimage, vol. 36, no. 3, pp. 785-792, March 2007.   DOI
7 T. Mima, N. Simpkins, T. Oluwatimilehin, and M. Hallett, "Force level modulates human cortical oscillatory activities," Neuroscience Letters, vol. 275, Issue. 2, pp. 77-80, 1999.   DOI
8 A. Broniec, "Control of cursor movement based on EEG motor cortex rhythm using autoregressive spectral analysis," Automatyka/Akademia Gorniczo-Hutnicza im. Stanislawa Staszica w Krakowie, vol. 15, pp. 321-329, 2011.
9 K. Y. Lee, T. H. Lee, and S. Y. Lee, "Motor Imagery Brain Signal Analysis for EEG-based Mouse Control," Journal of Cognitive Science, vol. 21, no. 2, pp. 309-338, 2010.   DOI
10 D. E. Kim, S. M. Park, and K. B. Sim, "Study on the Correlation between Grip Strength and EEG," Journal of Institute of Control, Robotics and Systems, vol. 19, no. 9, pp. 853-859, July. 2013.   DOI
11 Z. Chen, S. Haykin, J. J. Eggermont, and S. Becker, Correlative learning : a basis for brain and adaptive systems, John Wiley & Sons, 2008.
12 T. Yan, T. Jingtian, and G. Andong, "Multi-class EEG classification for brain computer interface based on CSP," IEEE International Conference on. BioMedical Engineering and Informatics, BMEI 2008, vol. 2, pp. 469-472, 2008.
13 B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," Proceeding of the fifth annual workshop on Computational learning theory, ACM, pp. 144-152, 1992.
14 H. G. Yeom and K. B. Sim, "Performance Improvements of Brain-Computer Interface Systems based on Variance-Considered Machines," Journal of Korean Institute of Intelligent Systems, vol. 20, no.1, pp. 153-158, 2010.   DOI
15 T. H. Nguyen, S. M. Park, K. E. Ko, and K. B. Sim, "Binary Classification Method using Invariant CSP for Hand Movements Analysis in EEG-based BCI System," Journal of Korean Institute of Intelligent Systems, vol. 23, no.2, pp. 178-183, 2013.   DOI