Online BCI Implementation of High-Frequency Phase Modulated Visual Stimuli

  • Danhua Zhu
  • Gary Garcia-Molina
  • Vojkan Mihajlović
  • Ronald M. Aarts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6766)

Abstract

Brain computer interfaces (BCI) that use the steady-state-visual-evoked-potential (SSVEP) as neural source, offer two main advantages over other types of BCIs: shorter calibration times and higher information transfer rates. SSVEPs elicited by high frequency (larger than 30 Hz) repetitive visual stimulation are less prone to cause visual fatigue, safer, and more comfortable for the user. However in the high frequency range there is a practical limitation because only few frequencies can elicit sufficiently strong SSVEPs for BCI purposes. We bypass this limitation by using only one stimulation frequency and different phases. To detect the phase from the recorded SSVEP, we use spatial filtering combined to phase synchrony analysis. We developed an online BCI implementation which was tested on six subjects and resulted on an average accuracy of 95.5% and an average bit rate of 34 bits-per-minute. Our approach has the advantage of entailing only minimal visual annoyance for the user.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Regan, D.: Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, Amsterdam (1989)Google Scholar
  2. 2.
    Cheng, M., Gao, X., Gao, S., Xu, D.: Design and implementation of a brain-computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering 49, 1181–1186 (2002)CrossRefGoogle Scholar
  3. 3.
    Gao, X., Xu, D., Cheng, M., Gao, S.: A BCI-based environmental controller for the motion-disabled. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 137–140 (2003)CrossRefGoogle Scholar
  4. 4.
    Lalor, E.C., Kelly, S.P., Finucane, C., Burke, R., Smith, R., Reilly, R.B., McDarby, G.: Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. Eurasip Journal on Applied Signal Processing 2005, 3156–3164 (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Friman, O., Lüth, T., Volosyak, I., Gräser, A.: Spelling with steady-state visual evoked potentials. In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, pp. 354–357 (2007)Google Scholar
  6. 6.
    Zhu, D., Bieger, J., Garcia Molina, G., Aarts, R.M.: A Survey of Stimulation Methods Used in SSVEP-Based BCIs. In: Computational Intelligence and Neuroscience (2010)Google Scholar
  7. 7.
    Fisher, R.S., Harding, G., Erba, G., Barkley, G.L., Wilkins, A.: Photic-and pattern-induced seizures: a review for the epilepsy foundation of America working group. Epilepsia 46, 1426–1441 (2005)CrossRefGoogle Scholar
  8. 8.
    Zhu, D., Garcia-Molina, G., Mihajlovic, V., Aarts, R.M.: Phase synchrony analysis in SSVEP-based BCIs. In: The 2nd International Conference on Computer Engineering and Technology (ICCET 2010), vol. 2, pp. 329–333 (2010)Google Scholar
  9. 9.
    Cheng, M., Gao, X., Gao, S., Xu, D.: Multiple color stimulus induced steady state visual evoked potentials. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 1012–1014 (2001)Google Scholar
  10. 10.
    Mukesh, T.M.S., Jaganathan, V., Reddy, M.R.: A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces. Physiological Measurement 27, 61–71 (2006)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Gao, X., Hong, B., Jia, C., Gao, S.: Brain-computer interfaces based on visual evoked potentials. IEEE Engineering in Medicine and Biology Magazine 27, 64–71 (2008)CrossRefGoogle Scholar
  12. 12.
    Jia, C., Gao, X., Hong, B., Gao, S.: Frequency and phase mixed coding in SSVEP-based brain-computer interface. IEEE Transactions on Biomedical EngineeringGoogle Scholar
  13. 13.
    Lee, P.L., Sie, J.J., Liu, Y.J., Wu, C.H., Lee, M.H., Shu, C.H., Li, P.H., Sun, C.W., Shyu, K.K.: An SSVEP-Actuated Brain Computer Interface Using Phase-Tagged Flickering Sequences: A Cursor System. Annals of Biomedical Engineering 38(7), 2383–2397 (2010)CrossRefGoogle Scholar
  14. 14.
    Wilson, J.J., Palaniappan, R.: Augmenting a SSVEP BCI through single cycle analysis and phase weighting. In: Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering, pp. 371–374 (2009)Google Scholar
  15. 15.
    Kluge, T., Hartmann, M.: Phase coherent detection of steady-state evoked potentials: Experimental results and application to brain-computer interfaces. In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, pp. 425–429 (2007)Google Scholar
  16. 16.
    Friman, O., Volosyak, I., Gräser, A.: Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Transactions on Biomedical Engineering 54, 742–750 (2007)CrossRefGoogle Scholar
  17. 17.
    Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)CrossRefGoogle Scholar
  18. 18.
    Parzen, E.: On Estimation of a Probability Function and Mode. Annals of Mathematical Statistics 33(3), 1065–1076 (1962)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions On Biomedical Engineering 51(6), 1034–1043 (2004)CrossRefGoogle Scholar
  20. 20.
    Biosemi, Biosemi system, http://www.biosemi.com
  21. 21.
    Garcia-Molina, G., Mihajlovic, V.: Spatial filters to detect Steady State Visual Evoked Potentials elicited by high frequency stimulation: BCI application. Journal of Biomedizinische Technik / Biomedical Engineering 55(3), 173–182 (2010)CrossRefGoogle Scholar
  22. 22.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006)CrossRefGoogle Scholar
  23. 23.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Danhua Zhu
    • 1
    • 2
    • 3
  • Gary Garcia-Molina
    • 1
  • Vojkan Mihajlović
    • 1
  • Ronald M. Aarts
    • 1
    • 2
  1. 1.Philips Research EuropeEindhovenThe Netherlands
  2. 2.Technical University EindhovenEindhovenThe Netherlands
  3. 3.College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina

Personalised recommendations