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Science China Information Sciences

, Volume 54, Issue 12, pp 2492–2498 | Cite as

Right-and-left visual field stimulation: A frequency and space mixed coding method for SSVEP based brain-computer interface

  • Zheng Yan
  • XiaoRong GaoEmail author
  • ShangKai Gao
Research Papers Special Focus

Abstract

On the condition of using limited frequencies, fewer targets could be presented in brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP). This paper proposes a novel coding method for SSVEP that, through a combination of frequency and spatial information, could increase the number of targets. Each target was composed of two flickers placed in the right-and-left visual fields. Given the role of the optic chiasm in the vision pathway, the two frequency components could be projected to contralateral occipital regions. Canonical correlation analysis (CCA) was utilized to detect the frequency components from the right or left visual cortex. Asymmetric index as a supplementary feature was also computed. Linear discriminant analysis (LDA) was used for target recognition. The attractive feature of this method is that it would substantially increase the number of targets. If the number of frequency was N, the method could present N times more targets than conventional SSVEP-based BCIs.

Keywords

brian-computer interface (BCI) frequency-space mixed coding steady-state visual evoked potential (SSVEP) 

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References

  1. 1.
    Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2: 157–180CrossRefGoogle Scholar
  2. 2.
    Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113: 767–791CrossRefGoogle Scholar
  3. 3.
    Regan D. Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. New York: Elsevier, 1989Google Scholar
  4. 4.
    Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng, 2002, 49: 1181–1186CrossRefGoogle Scholar
  5. 5.
    Gao X, Xu D, Cheng M, et al. A BCI-based environmental controller for the motion-disabled. IEEE Trans Neural Syst Rehabil Engineer, 2003, 11: 137–140CrossRefGoogle Scholar
  6. 6.
    Jia C, Gao X R, Hong B, et al. Frequency and phase mixed coding in SSVEP-based brain-computer interface. IEEE Trans Biomed Eng, 2011, 58: 200–206CrossRefGoogle Scholar
  7. 7.
    Kelly S P, Lalor E C, Finucane C, et al. Visual spatial attention control in an independent brain-computer interface. IEEE Trans Biomed Eng, 2005, 52: 1588–1596CrossRefGoogle Scholar
  8. 8.
    Materka A, Byczuk M, Poryzala P. A virtual keypad based on alternate half-field stimulated visual evoked potentials. In: Proceedings of the 2007 International Symposium on Information Technology Convergence, Washington DC, USA, 2007. 296–300Google Scholar
  9. 9.
    Yan Z, Gao X R, Bin G Y, et al. A half-field stimulation pattern for SSVEP-based brain-computer interface. In: Proceedings of the 31th IEEE/EMBS Annual International Conference, Minneapolis, Minnesota, USA, 2009Google Scholar
  10. 10.
    Brainard D H. The psychophysics toolbox. Spat Vis, 1997, 10: 443–446CrossRefGoogle Scholar
  11. 11.
    Hotelling H. Relations between two sets of variants. Biometrika, 1936, 28: 321–377zbMATHGoogle Scholar
  12. 12.
    Anderson T W. An Introduction to Multivariate Statistical Analysis. 2nd ed. New York: Wiley, 1984zbMATHGoogle Scholar
  13. 13.
    von Storch H, Zwiers F. Statistical Analysis in Climate Research. Cambridge: Cambridge University Press, 1999Google Scholar
  14. 14.
    Harmony T, Hinojosa G, Marosi E, et al. Correlation between EEG spectral parameters and an educational evaluation. Int J Neurosci, 1990, 55: 147–155CrossRefGoogle Scholar
  15. 15.
    Lin Z L, Zhang C S, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng, 2007, 54: 1172–1176CrossRefGoogle Scholar
  16. 16.
    Bin G Y, Yan Z, Gao X R, et al. An online multi-channel SSVEP-based BCI using CCA method. J Neural Eng, 2009, 6: 2–8CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijingChina

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