Advertisement

A Multi-channel SSVEP-Based Brain–Computer Interface Using a Canonical Correlation Analysis in the Frequency Domain

  • Guang Chen
  • Dandan Song
  • Lejian Liao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Brain–computer interface (BCI) is a new way for man–machine interaction with wide applications, in which steady-state visual evoked potentials (SSVEP) is a promising option. However, many characteristics of SSVEP show great user variation. So parameter optimization and channel selection for each subject were applied to improve the performance of BCI. These optimizations limit the practical applicability of the SSVEP-based BCI. The use of a canonical correlation analysis (CCA) method for multi-channel SSVEP in the time domain detection showed highly increased detection accuracy, but it is sensitive to the noise when the stimulate frequency is low. In this paper, a method of CCA in the frequency domain is presented for classifying multi-channel SSVEPs. First overlapping average is conducted on the original training signals. Then fast Fourier transform (FFT) is used to transform the signals from time domain to frequency domain to produce the reference data. Finally, according to the correlation coefficients of the new data and the references in the frequency domain, the SSVEP is classified. The experimental results show the enhanced accuracy of our method when applied to low stimulate frequencies.

Keywords

BCI SSVEP Overlapping average FFT CCA in frequency domain 

Notes

Acknowledgments

This work was supported in part by the National Science Foundation (Grant No. 61003168), Beijing Natural Science Foundation (Grant No. 4112050), and Excellent Researcher Award Program and Basic Research Foundation of Beijing Institute of Technology.

References

  1. 1.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791CrossRefGoogle Scholar
  2. 2.
    Wang Y, Wang R, Gao X, Gao S (2006) A practical VEP-based brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 14:234–240CrossRefGoogle Scholar
  3. 3.
    Middendorf M, McMillan G, Calhoun G, Jones K (2000) Brain–computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng 8:211–214CrossRefGoogle Scholar
  4. 4.
    Kelly S, Lalor E, Finucane C, McDarby G, Reilly R (2005) Visual spatial attention control in an independent brain–computer interface. IEEE Trans Biomed Eng 52:1588–1596CrossRefGoogle Scholar
  5. 5.
    Müller-Putz G, Scherer R, Brauneis C, Pfurtscheller G (2005) Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. Neural Eng 2:123–130Google Scholar
  6. 6.
    Cheng M, Gao X, Gao S (2005) Design and implementation of a brain–computer interface with high transfer rates. IEEE Trans Biomed Eng 49:1181–1186CrossRefGoogle Scholar
  7. 7.
    Friman O, Volosyak I, Graser A (2007) Multiple channel detection of steady-state visual evoked potentials for brain–computer interfaces. IEEE Trans Biomed Eng 54:742–750CrossRefGoogle Scholar
  8. 8.
    Sutter EE (1992) The brain response interface: communication through visually-induced electrical brain response. Microcomput Appl 15:31–45CrossRefGoogle Scholar
  9. 9.
    Trejo L, Rosipal R, Matthews B (2006) Brain–computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Trans Neural Syst Rehabil Eng 14:225–229CrossRefGoogle Scholar
  10. 10.
    Kluge T, Hartmann M (2007) Phase coherent detection of steady-state evoked potentials: experimental results and application to brain–computer interfaces. In: Proceedings of the 3rd international IEEE EMBS neural engineering conference, pp 425–429Google Scholar
  11. 11.
    Muller-Putz GR, Pfurtscheller G (2008) Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng 55:361–364CrossRefGoogle Scholar
  12. 12.
    Wu Z, Yao D (2008) Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs. IEEE J Neural Eng 5:36–43CrossRefGoogle Scholar
  13. 13.
    Lin Z, Zhang C, Wu W, Gao X (2008) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 53:2610–2614Google Scholar
  14. 14.
    Storch H, Zwiers F (2002) Statistical analysis in climate research. Cambridge University Press, Cambridge Google Scholar
  15. 15.
    Friman O, Cedefamn J, Lundberg P, Borga M, Knutsson H (2001) Detection of neural activity in functional MRI using canonical correlation analysis. Magn Reson Med 45:323–330CrossRefGoogle Scholar
  16. 16.
    Pan J, Gao X, Duan F et al (2011) Enhancing the classification accuracy of steady-state visual evoked potential-based brain–computer interfaces using phase constrained canonical correlation analysis. J Neural Eng 8(3): 036–027 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of TechnologyBeijingChina

Personalised recommendations