A MUSIC-based method for SSVEP signal processing

  • Kun Chen
  • Quan LiuEmail author
  • Qingsong Ai
  • Zude Zhou
  • Sheng Quan Xie
  • Wei Meng
Scientific Paper


The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100 %. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.


Brain computer interface (BCI) Steady state visual evoked potential (SSVEP) Multiple signal classification (MUSIC) Feature extraction 



This work was supported by the National Science Foundation (Grant No. 51475342). The authors would like to thank all subjects for their participating in the experiments.


  1. 1.
    Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–173CrossRefPubMedGoogle Scholar
  2. 2.
    Mason SG, Birch GE (2003) A general framework for brain-computer interface design. IEEE Trans Neural Syst Rehabil Eng 11(1):70–85CrossRefPubMedGoogle Scholar
  3. 3.
    Wang YWR, Gao X (2006) A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 14(2):234–240CrossRefPubMedGoogle Scholar
  4. 4.
    Picton T (1990) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. J Clin Neurophysiol 7:450–451CrossRefGoogle Scholar
  5. 5.
    Bin GY, Gao XR, Yan Z, Hong B, Gao SK (2009) An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J. Neural Eng. 6 (4):1–6 (046002)Google Scholar
  6. 6.
    Liu Q, Chen K, Ai Q, Xie SQ (2014) Review: recent development of signal processing algorithms for ssvep-based brain computer interfaces. J Med Biol Eng 34(4):299–309CrossRefGoogle Scholar
  7. 7.
    Muller-Putz GR, Pfurtscheller G (2008) Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng 55(1):361–364CrossRefPubMedGoogle Scholar
  8. 8.
    Bian Y, Li HW, Zhao L, Yang GH, Geng LQ (2011) Research on steady state visual evoked potentials based on wavelet packet technology for brain-computer interface. Proc Eng 15:2629–2633CrossRefGoogle Scholar
  9. 9.
    Zhang Z, Li X, Deng Z (2010) A CWT-based SSVEP classification method for brain-computer interface system. In: Internation conference on intelligent control and information processing, pp. 43–48Google Scholar
  10. 10.
    Yan B, Li Z, Li H, Yang G, Shen H (2010) Research on brain-computer interface technology based on steady state visual evoked potentials. In: 4th international conference on bioinformatics and biomedical engineering, pp. 1–4Google Scholar
  11. 11.
    Wu CH, Chang HC, Lee PL, Li KS, Sie JJ, Sun CW, Yang CY, Li PH, Deng HT, Shyu KK (2011) Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing. J Neurosci Methods 196(1):170–181CrossRefPubMedGoogle Scholar
  12. 12.
    Wu CH, Chang HC, Lee PL (2009) Instantaneous gaze-target detection by empirical mode decomposition: application to brain computer interface. In: World congress on medical physics and biomedical engineering, pp. 215–218Google Scholar
  13. 13.
    Friman O, Luth T, Volosyak I, Graser A (2007) Spelling with steady-state visual evoked potentials. In: 3rd international IEEE/EMBS conference on neural engineering, pp. 354–357Google Scholar
  14. 14.
    Volosyak I (2011) SSVEP-based bremen-BCI interface—boosting information transfer rates. J Neural Eng 8 (3):1–11 (036020)Google Scholar
  15. 15.
    Volosyak I, Malechka T, Valbuena D, Graser A (2010) A novel calibration method for SSVEP based brain-computer interfaces. In: 18th European signal processing conference pp. 939–943Google Scholar
  16. 16.
    Cecotti H (2010) A self-paced and calibration-less SSVEP-based brain-computer interface speller. IEEE Trans Neural Syst Rehabil Eng 18(2):127–133CrossRefPubMedGoogle Scholar
  17. 17.
    Zhang ZM, Deng ZD (2012) A kernel canonical correlation analysis based idle-state detection method for SSVEP-based brain-computer interfaces. In: 2nd international conference on material and manufacturing technology pp. 634–640Google Scholar
  18. 18.
    Lin ZL, Zhang CS, Wu W, Gao XR (2006) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 53(12):2610–2614CrossRefPubMedGoogle Scholar
  19. 19.
    Hakvoort G, Reuderink B, Obbink M (2011) Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system. Centre for telematics and information technology, University of Twente.
  20. 20.
    Schmidt RO (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propag 34(3):276–280CrossRefGoogle Scholar
  21. 21.
    Swami A, Mendel JM, Nikias CL (1998) Higher-order spectral analysis toolbox: for use with MATLAB: user’s guide. Mathworks, IncorporatedGoogle Scholar
  22. 22.
    Wang Yongliang CH, Yingning Peng, Qun Wan (2004) Theories and algorithms of spatial spectrum estimation. Press of Tsinghua University, TsinghuaGoogle Scholar
  23. 23.
    Hardoon DRSS, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefPubMedGoogle Scholar
  24. 24.
    Chen K, Liu Q, Ai QS (2014) Multi-channel SSVEP pattern recognition based on MUSIC. In: 4th international conference on intelligent structure and vibration control pp. 84–88Google Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2016

Authors and Affiliations

  • Kun Chen
    • 1
    • 3
  • Quan Liu
    • 2
    • 3
    Email author
  • Qingsong Ai
    • 2
    • 3
  • Zude Zhou
    • 1
    • 3
  • Sheng Quan Xie
    • 4
  • Wei Meng
    • 2
    • 3
  1. 1.School of Mechanical and Electronic EngineeringWuhan University of TechnologyWuhanChina
  2. 2.School of Information EngineeringWuhan University of TechnologyWuhanChina
  3. 3.Key Laboratory of Fiber Optic Sensing Technology and Information Processing Ministry of EducationWuhanChina
  4. 4.Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand

Personalised recommendations