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

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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.

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References

  1. Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2: 157–180

    Article  Google Scholar 

  2. Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113: 767–791

    Article  Google Scholar 

  3. Regan D. Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. New York: Elsevier, 1989

    Google Scholar 

  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: 1181–1186

    Article  Google Scholar 

  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: 137–140

    Article  Google Scholar 

  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: 200–206

    Article  Google Scholar 

  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: 1588–1596

    Article  Google Scholar 

  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. 296–300

  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, 2009

  10. Brainard D H. The psychophysics toolbox. Spat Vis, 1997, 10: 443–446

    Article  Google Scholar 

  11. Hotelling H. Relations between two sets of variants. Biometrika, 1936, 28: 321–377

    MATH  Google Scholar 

  12. Anderson T W. An Introduction to Multivariate Statistical Analysis. 2nd ed. New York: Wiley, 1984

    MATH  Google Scholar 

  13. von Storch H, Zwiers F. Statistical Analysis in Climate Research. Cambridge: Cambridge University Press, 1999

    Google Scholar 

  14. Harmony T, Hinojosa G, Marosi E, et al. Correlation between EEG spectral parameters and an educational evaluation. Int J Neurosci, 1990, 55: 147–155

    Article  Google Scholar 

  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: 1172–1176

    Article  Google Scholar 

  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: 2–8

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China

    Zheng Yan, XiaoRong Gao & ShangKai Gao

Authors
  1. Zheng Yan
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  2. XiaoRong Gao
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  3. ShangKai Gao
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Corresponding author

Correspondence to XiaoRong Gao.

Additional information

YAN Zheng was born in 1981. He received the B.S. degree in biophysics from Nankai University in 2004, the M.S. degree in biophysics from Institute of Modern Physics, Chinese Academy of Sciences in 2007. Currently, he is a Ph.D. candidate in biomedical engineering, Tsinghua University.

GAO XiaoRong was born in Beijing, China in 1963. He received the B.S. degree in biomedical engineering from Zhejiang University in 1986, the M.S. degree in biomedical engineering from Peking Union Medical College in 1989, and the Ph.D. degree in biomedical engineering from Tsinghua University in 1992. He is currently a professor of the Department of Biomedical Engineering, Tsinghua University. His current research interests are biomedical signal processing and medical instrumentation, especially the study of brain-computer interface.

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Yan, Z., Gao, X. & Gao, S. Right-and-left visual field stimulation: A frequency and space mixed coding method for SSVEP based brain-computer interface. Sci. China Inf. Sci. 54, 2492–2498 (2011). https://doi.org/10.1007/s11432-011-4503-5

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  • DOI: https://doi.org/10.1007/s11432-011-4503-5

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