A Novel Combination of Time Phase and EEG Frequency Components for SSVEP-Based BCI

  • Jing Jin
  • Yu Zhang
  • Xingyu Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7062)


The steady-state visual evoked potential (SSVEP) has been widely applied in brain-computer interfaces (BCIs), such as letter or icon selection and device control. Most of these BCIs used different flickering frequencies to evoke SSVEP with different frequency components that were used as control commands. In this paper, a novel method combining the time phase and EEG frequency components is presented and validated with nine healthy subjects. In this method, four different frequency components of EEG were classified out from four time phases. When the SSVEP is evoked and what is the frequency of the SSVEP is determined by the linear discriminant analysis (LDA) classifier in the same time to locate the target image. The results from offline analysis show that this method yields good performance both in classification accuracy and information transfer rate (ITR).


Brain-computer interfaces (BCIs) Electroencephalogram (EEG) Steady-state visual evoked potential (SSVEP) Time phase 


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  1. 1.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Zhu, D.H., Bieger, J., Molina, G.G., Aarts, R.M.: A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. (2010), doi: 10.1155/2010/702357Google Scholar
  3. 3.
    Zhang, Y., Jin, J., Qing, X., Wang, B., Wang, X.: LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomed. Signal Process. Control (2011), doi:10.1016/j.bspc.2011.02.002Google Scholar
  4. 4.
    Müller-Putz, G.R., Scherer, R., Brauneis, C., Pfurtscheller, G.: Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural Eng. 2, 123–130 (2005)CrossRefGoogle 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 (CNE 2007), Hawaii, May 2-5, pp. 510–523 (2007)Google Scholar
  6. 6.
    Gao, X., Xu, D., Cheng, M., Gao, S.: A BCI-based environmental controller for the motion-disabled. IEEE Trans. Neural Syst. Rehabi. Eng. 11, 137–140 (2003)CrossRefGoogle Scholar
  7. 7.
    Graimann, B., Allison, B., Mandel, C., Lüth, T., Valbuena, D., Gräser, A.: Non-invasive brain-computer interfaces for semi-autonomous assistive devices. In: Schuster, A. (ed.) Robust Intelligent System, pp. 113–138. Springer, London (2008)CrossRefGoogle Scholar
  8. 8.
    Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429–2437 (2004)CrossRefGoogle Scholar
  9. 9.
    Cheng, M., Gao, X., Gao, S., Xu, D.: Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans. Biomed. Eng. 49, 1181–1186 (2002)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S.: A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehab. Eng. 14, 234–239 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jing Jin
    • 1
  • Yu Zhang
    • 1
    • 2
  • Xingyu Wang
    • 1
  1. 1.School of Information Science and EngineeringEast China University of Science and TechnologyShanghaiChina
  2. 2.Laboratory for Advanced Brain Signal ProcessingRIKEN, Brain Science InstituteSaitamaJapan

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