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

  • 제19권3호
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  • Pages.227-232
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  • 2013
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  • 1976-5622(pISSN)
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  • 2233-4335(eISSN)
제어로봇시스템학회 (Institute of Control, Robotics and Systems)
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동작 상상-P300 기반 BCI 환경에서의 로봇 제어 실용화 기술

Practical Use Technology for Robot Control in BCI Environment based on Motor Imagery-P300

  • 김용훈 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 박승민 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • 투고 : 2013.01.09
  • 심사 : 2013.02.15
  • 발행 : 2013.03.01
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초록

BCI (Brain Computer Interface) is technology to control external devices by measuring the brain activity, such as electroencephalogram (EEG), so that handicapped people communicate with environment physically using the technology. Among them, EEG is widely used in various fields, especially robot agent control by using several signal response characteristics, such as P300, SSVEP (Steady-State Visually Evoked Potential) and motor imagery. However, in order to control the robot agent without any constraint and precisely, it should take advantage of not only a signal response characteristic, but also combination. In this paper, we try to use the fusion of motor imagery and P300 from EEG for practical use of robot control in BCI environment. The results of experiments are confirmed that the recognition rate decreases compared with the case of using one kind of features, whereas it is able to classify each both characteristics and the practical use technology based on mobile robot and wireless BCI measurement system is implemented.

키워드

참고문헌

  1. A. Kubler, N. Neumann, J. Kaiser, B. kotchoubey, T. Hinterberger, and N, Birbamer, "Brain-computer communication: self-regulation of slow cortical potentials for verbal communication," Archives of Physical Medicine and Rehabilitation, vol. 82, no. 11, pp. 1533-1539, Nov. 2001. https://doi.org/10.1053/apmr.2001.26621
  2. C. Guger, R. Leeb, D. Friedman, V. Vinayagamoorthy, G. Edlinger, and M. Slater, "Controlling virtual environments by thoughts," Clinical Neurophysiology, vol. 118, no. 4, pp. e36, Mar. 2007.
  3. M. Tangermann, M. Krauledat, K. Grzeska, M. Sagebaum, B. Blankertz, C. Vidaurre, and K-R Muller, "Playing pinball with non-invasive BCI," Advances in Neural Information Processing System, vol. 21, pp. 1641-1648, 2009.
  4. J. R. Bach, "Amyotrophic lateral sclerosis- communication status and survival with ventilatory support," American Journal of physical Medicine & Rehabilitation / Association of Academic Physiatrists, vol. 72, no. 6, pp. 343-349, Dec. 1993.
  5. E. Buch, C. Weber, L. G. Cohen, C. Braun, M. Dimyan, T. Ard, J. Mellinger, A. Caria, S. Soekadar, A. Fourkas, and N. Birbaumer, "Think to move: A neuromagnetic BCI (Brain-Computer Interface) system for chronic stroke," Stroke, vol. 39, no. 3, pp. 910-917, Feb. 2008. https://doi.org/10.1161/STROKEAHA.107.505313
  6. J. Allen, "Shot time spectral analysis, synthesis, and modification by discrete fourier transform," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 25, pp. 235-238, Jun. 1977. https://doi.org/10.1109/TASSP.1977.1162950
  7. G. Townsend, B. K, Lapallo, C. B. Boulay, D. J. Krusienski, G. E. Frye, C. K. Hauser, N. E. Schwartz, T. M. Vaughan, J. R. Wolpaw, and E. W. Sellers, "A novel P300-based brain-computer interface stimulus presentation paradigm: Moving beyond row and columns," Clinical Neurophysiology, vol. 121, pp. 1109-1120, 2010. https://doi.org/10.1016/j.clinph.2010.01.030
  8. E. Donnchine, K. M. Spencet, and R. Wijesinghe, "The mental prosthesis: assessing the speed of a P300-based brain-computer interface," IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, June 2000. https://doi.org/10.1109/86.847808
  9. V. Jeyabalan, A. Samraj, and C. K. Loo, "Motor imaginary- based brain-machine interface design using programmable logic controllers for the disabled," Comput Methods Biomech Biomed Engin. vol. 5, pp. 617-23, Oct. 2010.
  10. K. E. Ko and K. B. Sim, "HSA-based HMM optimization method for analyzing EEG pattern of motor imagery," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 8, pp. 747-752, Aug. 2011. https://doi.org/10.5302/J.ICROS.2011.17.8.747
  11. B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, and G. Curio, "The non-invasive berlin brain-computer interface: fast acquisition of effective performance in untrained subjects," NeuroImage, vol. 37, no. 2, pp. 539-550, Aug. 2007. https://doi.org/10.1016/j.neuroimage.2007.01.051
  12. J. H. Im, S. H. You, G. I. Jee, and D. H. Lee, "A path generation algorithm for obstacle avoidance in waypoint navigation of unmanned ground vehicle," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 8, pp. 843-850, Aug. 2011. https://doi.org/10.5302/J.ICROS.2011.17.8.843

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  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