Auditory Brain-Computer/Machine-Interface Paradigms Design

  • Tomasz M. Rutkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6851)

Abstract

The paper discusses novel and interesting, from users’ point of view, design of auditory brain-computer/machine interfaces (BCI/ BMI) utilizing human auditory responses. Two concepts of auditory stimuli BCI/BMI are presented. The first paradigm is based on steady-state tonal or musical stimuli yielding satisfactory EEG response classification for several seconds long stimuli. The second discussed paradigm is based on spatial sound localization and the brain evoked responses estimation, requiring shorter than a second stimuli presentation. In conclusion the preliminary results are discussed and suggestions for further applications are drawn.

Keywords

brain-computer-interface brain-machine-interface auditory neuroscience 

References

  1. 1.
    Bendor, D., Wang, X.: Differential neural coding of acoustic flutter within primate auditory cortex. Nature Neuroscience 10, 763–771 (2007)CrossRefGoogle Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines (2007)Google Scholar
  3. 3.
    Chen, M., Rutkowski, T.M., Jelfs, B., Souretis, G., Cao, J., Mandic, D.: Assessment of nonlinearity in brain electrical activity: A DVV approach. In: Proceedings of The 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing, March 3-7, pp. 461–464. Shanghai Jiao Tong University, Shanghai (2007)Google Scholar
  4. 4.
    Cichocki, A., Washizawa, Y., Rutkowski, T., Bakardjian, H., Phan, A.H., Choi, S., Lee, H., Zhao, Q., Zhang, L., Li, Y.: Noninvasive BCIs: Multiway signal-processing array decompositions. Computer 41(10), 34–42 (2008)CrossRefGoogle Scholar
  5. 5.
    Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., Edlinger, G.: How many people are able to control a P300-based brain-computer interface (BCI)? Neuroscience Letters 462(1), 94–98 (2009)CrossRefGoogle Scholar
  6. 6.
    Hjorth, B.: EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology 29, 306–310 (1970)CrossRefGoogle Scholar
  7. 7.
    Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N.C., Tung, C., Liu, H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 454(1971), 903–995 (1988)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Niedermeyer, E., Da Silva, F.L. (eds.): Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th edn. Lippincott Williams & Wilkins, Baltimore (2004)Google Scholar
  9. 9.
    Plourde, G.: Auditory evoked potentials. Best Practice & Research Clinical Anaesthesiology 20(1), 129–139 (2006)CrossRefGoogle Scholar
  10. 10.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008), http://www.R-project.org, http://www.R-project.org
  11. 11.
    Ross, B., Picton, T.W., Herdman, A.T., Hillyard, S.A., Pantev, C.: The effect of attention on the auditory steady-state response. Neurology and Clinical Neurophysiology 22, 1–4 (2004)Google Scholar
  12. 12.
    Rutkowski, T.M., Cichocki, A., Mandic, D.P.: Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach. In: Signal Processing Techniques for Knowledge Extraction and Information Fusion. Signals and Communication, pp. 261–274. Springer, Heidelberg (April 2008)CrossRefGoogle Scholar
  13. 13.
    Rutkowski, T.M., Cichocki, A., Mandic, D.P.: Spatial auditory paradigms for brain computer/machine interfacing. In: Proceedings of International Workshop on the Principles And Applications of Spatial Hearing 2009 (IWPASH 2009), Miyagi-Zao Royal Hotel, Sendai, Japan, p. 5 (November 11-13, 2009), http://dx.doi.org/10.1142/9789814299312_0025
  14. 14.
    Rutkowski, T.M., Cichocki, A., Tanaka, T., Mandic, D.P., Cao, J., Ralescu, A.L.: Multichannel spectral pattern separation - an EEG processing application. In: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), pp. 373–376. IEEE Press, Taipei (2009)CrossRefGoogle Scholar
  15. 15.
    Rutkowski, T.M., Tanaka, T., Zhao, Q., Cichocki, A.: Spatial auditory BCI/BMI paradigm - Multichannel EMD approach to brain responses estimation. In: Proceedings of the Second APSIPA Annual Summit and Conference (APSIPA ASC 2010), December 14-17, pp. 197–202. APSIPA, Biopolis (2010)Google Scholar
  16. 16.
    Schlögl, A., Brunner, C.: Biosig: A free and open source software library for BCI research. Computer 41(10), 44–50 (2008)CrossRefGoogle Scholar
  17. 17.
    Schreuder, M., Blankertz, B., Tangermann, M.: A new auditory multi-class brain-computer interface paradigm: Spatial hearing as an informative cue. PLoS ONE 5(4), e9813 (2010), http://dx.doi.org/10.1371%2Fjournal.pone.0009813 CrossRefGoogle Scholar
  18. 18.
    Stach, B.A.: The auditory steady-state response: A primer. The Hearing Journal 55(9), 10–18 (2002)CrossRefGoogle Scholar
  19. 19.
    Wellcome Trust Centre for Neuroimaging: Statistical parametric mapping - SPM8 package (2010), http://www.fil.ion.ucl.ac.uk/spm/, http://www.fil.ion.ucl.ac.uk/spm/
  20. 20.
    Zhao, Q., Rutkowski, T., Zhang, L., Cichocki, A.: Generalized optimal spatial filtering using a kernel approach with application to EEG classification. Cognitive Neurodynamics 4(4), 355–358 (2010), http://dx.doi.org/10.1007/s11571-010-9125-x CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tomasz M. Rutkowski
    • 1
    • 2
  1. 1.Life Science Center of TARAUniversity of TsukubaTsukubaJapan
  2. 2.RIKEN Brain Science InstituteWako-shiJapan

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