An Auditory Oddball Based Brain-Computer Interface System Using Multivariate EMD

  • Qiwei Shi
  • Wei Zhou
  • Jianting Cao
  • Danilo P. Mandic
  • Toshihisa Tanaka
  • Tomasz M. Rutkowski
  • Rubin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6216)

Abstract

A brain-computer interface (BCI) is a communication system that allows users to act on their environment by using only brain-activity. This paper presents a novel design of the auditory oddball task based brain-computer interface (BCI) system. The subject is presented with a stimulus presentation paradigm in which low-probability auditory targets are mixed with high-probability ones. In the data analysis, we employ a novel algorithm based on multivariate empirical mode decomposition that is used to extract informative brain activity features through thirteen electrodes’ recorded signal of each single electroencephalogram (EEG) trial. Comparing to the result of arithmetic mean of all trials, auditory topography of peak latencies of the evoked event-related potential (ERP) demonstrated that the proposed algorithm is efficient for the detection of P300 or P100 component of the ERP in the subject’s EEG. As a result we have found new ways to process EEG signals to improve detection for a P100 and P300 based BCI system.

Keywords

Electroencephalography (EEG) Multivariate empirical mode decomposition Auditory oddball P300 Brain-computer interface (BCI) 

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References

  1. 1.
    Wolpaw, J.R., Birbaumer, N., Mcfarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-Computer Interfaces for Communication and Control. Clinical Neurophysiology 113, 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Cichocki, A., Washizawa, Y., Rutkowski, T., Bakardjian, H., Phan, A., Choi, S., Lee, H., Zhao, Q., Zhang, L., Li, Y.: Noninvasive BCIs: Multiway Signal-Processing Array Decompositions. Computer 41, 34–42 (2008)CrossRefGoogle Scholar
  3. 3.
    Sajda, P., Mueller, K.-R., Shenoy, K.V.: Brain Computer Interfaces. IEEE Signal Processing Magazine, Special issue, 16–17 (January, 2008)Google Scholar
  4. 4.
    Rutkowski, T.M., Vialatte, F., Cichocki, A., Mandic, D.P., Barros, A.K.: Auditory Feedback for Brain Computer Interface Management - An EEG Data Sonification Approach. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4253, pp. 1232–1239. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Farwell, L.A., Donchin, E.: Talking off the top of your head: Toward a Mental Prosthesis Utilizing Event-related Brain Potentials. Electroencephalography and Clinical Neurophysiology 70, 512–523 (1988)CrossRefGoogle Scholar
  6. 6.
    Gao, X., Xu, D., Cheng, M., Gao, S.: A BCI-based Environmental Controller for The Motion-disabled. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 137–140 (2003)CrossRefGoogle Scholar
  7. 7.
    Rehman, N., Mandic, D.P.: Multivariate Empirical Mode Decomposition. Proceedings of the Royal Society A (2010) (in print), http://www.commsp.ee.ic.ac.uk/~mandic/research/emd.htm
  8. 8.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The Empirical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. Proceedings of the Royal Society of London, A 454, 903–995 (1998)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Huang, N.E., Wu, M., Long, S., Shen, S., Qu, W., Gloersen, P., Fan, K.: A Confidence Limit for The Empirical Mode Decomposition and Hilbert Spectral Analysis. Proc. R. Soc. Lond. A 459, 2317–2345 (2003)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Tanaka, T., Mandic, D.P.: Complex Empirical Mode Decomposition. IEEE Signal Processing Letters 14, 101–104 (2006)CrossRefGoogle Scholar
  11. 11.
    Altaf, M.U., Gautama, T., Tanaka, T., Mandic, D.P.: Rotation Invariant Complex Empirical Mode Decomposition. In: Proc. IEEE Int. Conf. on Acoustics, Speech, Signal Processing, Honolulu, HI, pp. 1009–1012 (2007)Google Scholar
  12. 12.
    Rilling, G., Flandrin, P., Goncalves, P., Lilly, J.M.: Bivariate Empirical Mode Decomposition. IEEE Signal Process. Lett. 14, 936–939 (2007)CrossRefGoogle Scholar
  13. 13.
    Rehman, N., Mandic, D.P.: Empirical Mode Decomposition for Trivariate Signals. IEEE T. Signal Process (in print)Google Scholar
  14. 14.
    Sharbrough, F., Chatrian, C.E., Lesser, R.P., Luders, H., Nuwer, M., Picton, T.W.: American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature. Journal of Clinical Neurophysiology 8, 200–202 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qiwei Shi
    • 1
  • Wei Zhou
    • 1
  • Jianting Cao
    • 1
    • 4
    • 5
  • Danilo P. Mandic
    • 2
  • Toshihisa Tanaka
    • 3
    • 4
  • Tomasz M. Rutkowski
    • 4
  • Rubin Wang
    • 5
  1. 1.Saitama Institute of TechnologySaitamaJapan
  2. 2.Imperial College LondonLondonU.K.
  3. 3.Tokyo University of Agriculture and TechnologyTokyoJapan
  4. 4.Brain Science Institute, RIKENSaitamaJapan
  5. 5.East China University of Science and TechnologyShanghaiChina

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