Browse > Article

Brain-Machine Interface Using P300 Brain Wave  

Cha, Kab-Mun (Information and Telecommunication Engineering, School of IT, Soongsil University)
Shin, Hyun-Chool (Information and Telecommunication Engineering, School of IT, Soongsil University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.47, no.5, 2010 , pp. 18-23 More about this Journal
Abstract
In this paper, we propose a computationally efficient method detecting the P300 wave for brain-machine interface. Electrophysiological researches have shown that the P300 wave's potential is decreased when human intention matches visual stimulation. Motivated by this fact, we can infer human intention for brain-machine interface by detecting the P300 wave's potential decrease. The P300 wave is recorded from EEG(electroencephalogram) electrodes attached on human brain skull after giving alphabetical stimulation. To detect the potential decrease in P300, firstly we statistically model the P300 wave's negative potential. Then we infer human intention based on maximum likelihood estimation. The proposed method was evaluated on the data recorded from three healthy human subjects. The method achieved an averaging accuracy of 98% from subject k, 90% from subject j and 79.8% from subject h.
Keywords
BMI; BCI; EEG; P300; Maximum likelihood; EP;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. R. Wolpaw, D. J. McFarland, and T. M. vaughan, "Brain-computer interface research at the Wadsworth center," IEEE Trans. Rehab. Eng., vol. 8, no. 2, pp. 222-226, June 2000.   DOI   ScienceOn
2 A. Lenhardt, M. Kaper, and H. J. Ritter, "An adaptive P300-based online brain-computer interface." IEEE Trans. Neural Syst. Rehab. Eng., vol. 16, no. 2, pp. 121-130, April 2008.   DOI
3 B. J. Culpepper and R. M. Keller, "Enabling computer decisions based on EEG input," IEEE Trans. Rehab. Eng., vol. 11, no. 4, December 2003.
4 B. Blankertz, G. Dornhege, M. Krauledat, K. R. Muller, V. Kunzmann, F. Losch, and G. Curio, "The berlin Brain-compouter interface: EEG-based communication without subject training," IEEE Trans. Rehab. Eng., vol. 14, no. 2, June 2006.
5 M. Thulasidas, C. Guan, and J. Wu, "Robust classification of EEG signal for brain-computer interface," IEEE Trans. Neural. Syst. Rehab. Eng., vol. 14, no. 1, pp. 24-29, March 2006.   DOI   ScienceOn
6 H. Zhang, C. Guan, and C. Wang, "Asynchronous P300-based brain-computer interfaces: A computational approach with statistical models," IEEE Trans. Biomed. eng., vol. 55, no. 6, June 2008.
7 P. A. Karjalainen, J. P. Kaipio, A. S. Koistinen, and M. vauhkonen, "Subspace regularization method for the single-trial estimation of evoked potentials," IEEE Trans. Biomed. Eng., vol. 46, no. 7, July 1999.
8 G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlogl, B. Obermaier, and M. Pregenzer, "Current trends in Graz brain-computer interface(BCI) research," IEEE Trans. Rehab. Eng., vol. 8, no. 2, pp. 216-219, June 2000.   DOI   ScienceOn
9 J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. vaughan, "Brain-computer interfaces for communication and control," Clin. Neurophysiol., vol. 113, pp. 767-791, 2002.   DOI   ScienceOn
10 J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, "An EEG-based brain-computer interface for cursor control," Electro-encephalogr. clin. Neurophysiol., vol. 78, pp. 252-259, 1991.   DOI   ScienceOn