SpringerLink
Log in

An Automatic SSVEP Component Selection Measure for High-Performance Brain-Computer Interface

  • Conference paper
  • First Online:
Foundations and Practical Applications of Cognitive Systems and Information Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 215))

Abstract

This paper proposed an automatic steady-state visual evoked potential (SSVEP) component selection (SCS) measure for a high-performance SSVEP-based brain-computer interface (SBCI) system. First, multi-electrode raw electroencephalogram signals are spatially pre-processed using a blind source separation technique resulting in multi-source components. The SCS measure of each component is then calculated by continuous wavelet transform (CWT), and the ensemble features that contain the weighted CWT energy of individual SSVEP harmonic are extracted. Second, the SSVEP component with maximal SCS measure is considered to have the highest signal-to-noise ratio. In our SBCI system, six stimulus frequencies served as the input patterns. Offline analyses were performed, through which the common electrode locations, the time window size, and the number of harmonics were defined. Thereafter the results of our method were compared with those of others. We next carried out an online test of the SBCI for 11 subjects using eight common electrode locations, a 1.5-s time window, and the first and second harmonics. The test results showed that our method achieved an average accuracy of 95.2 % and a practical bit rate of 68.2 bits/min.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791

    Article  Google Scholar 

  2. Lalor EC, Kelly SP, Finucane C, Burke R, Smith R, Reilly RB, McDarby G (2005) Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP J Adv Signal Process 2005(19):3156–3164

    Article  MATH  Google Scholar 

  3. Müller-Putz GR, Scherer R, Brauneis C, Pfurtscheller G (2005) Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng 2:123–130

    Article  Google Scholar 

  4. Shyu KK, Lee PL, Liu YJ, Sie JJ (2010) Dual-frequency steady-state visual evoked potential for brain computer interface. Neurosci Lett 483(1):28–31

    Article  Google Scholar 

  5. Shyu KK, Lee PL, Lee MH, Lin MH, Lai RJ, Chiu YJ (2010) Development of a low-cost FPGA-based SSVEP BCI multimedia control system. IEEE Trans Biomed Circuits Syst 4(2):125–132

    Article  Google Scholar 

  6. Lopez-Gordo MA, Prieto A, Pelayo F, Morillas C (2010) Use of phase in brain-computer interfaces based on steady-state visual evoked potentials. Neural Process Lett 32(1):1–9

    Article  Google Scholar 

  7. Luo A, Sullivan TJ (2010) A user-friendly SSVEP-based brain-computer interface using a time-domain classifier. J Neural Eng 7(2)

    Google Scholar 

  8. Cheng M, Gao X, Gao S (2002) Design and implementation of a brain–computer interface with high transfer rates. IEEE Trans Biomed Eng 49(10):1181–1186

    Article  Google Scholar 

  9. Zhang ZM, Li XQ, Deng ZD (2010) A CWT-based SSVEP classification method for brain-computer interface system. In: Proceedings of the IEEE international conference on intelligent control and information processing. Dalian, pp 43–48

    Google Scholar 

  10. Jia C, Gao X, Hong B, Gao S (2011) Frequency and phase mixed coding in SSVEP-based brain—computer interface. IEEE Trans Biomed Eng 58(1):200–206

    Article  Google Scholar 

  11. Brunner C, Allison BZ, Krusienski DJ, Kaiser V, Müller-Putz GR, Pfurtscheller G, Neuper C (2010) Improved signal processing approaches in an offline simulation of a hybrid brain-computer interface. J Neurosci Meth 188(1):165–173

    Article  Google Scholar 

  12. Müller-Putz GR, Eder E, Wriessnegger SC, Pfurtscheller G (2008) Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI. J Neurosci Meth 168(1):174–181

    Article  Google Scholar 

  13. Wang YJ, Wang RP, Gao XR, Hong B, Gao SK (2006) A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 14(2):234–239

    Article  Google Scholar 

  14. Wu ZH, Yao DH (2008) Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs. J Neural Eng 5(1):36–43

    Article  Google Scholar 

  15. 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(4):742–750

    Article  Google Scholar 

  16. Bin G, Gao X, Yan Z, Hong B, Gao S (2009) An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng 6(4):046002

    Google Scholar 

  17. Pan J, Gao X, Duan F, Yan Z, Gao S (2011) Enhancing the classification accuracy of steady-state visual evoked potential-based brain—computer interfaces using phase. J Neural Eng 8(3):036027. doi:10.1088/1741-2560/8/3/036027

    Google Scholar 

  18. Yan Z, Gao X, Bin G, Hong B, Gao S (2009) A half-field stimulation pattern for SSVEP-based brain-computer interface. Proc IEEE Eng Med Biol Soc 2009(2006):6461–6464

    Google Scholar 

  19. Peters BO, Pfurtscheller G, Flyvbjerg H (2001) Automatic differentiation of multichannel EEG signals. IEEE Trans Biomed Eng 48(1):111–116

    Article  Google Scholar 

  20. Regan D (1989) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New York

    Google Scholar 

  21. Cichocki A, Amari S (2002) Adaptive blind signal and image processing: learning algorithms and applications (ch. 4). Wiley, Chichester

    Google Scholar 

  22. Belouchrani A, Abed-Meraim K, Cardoso J-F, Moulines E (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45(2):434–444

    Article  Google Scholar 

  23. Goupillaud P, Grossman A, Morlet J (1984) Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23(1):85–102

    Article  Google Scholar 

  24. Wang SY, Aziza TZ, Steina JF, Liu X (2005) Time–frequency analysis of transient neuromuscular events: dynamic changes in activity of the subthalamic nucleus and forearm muscles related to the intermittent resting tremor. J Neurosci Meth 145(1–2):151–158

    Article  Google Scholar 

  25. Cortes C, Vapnik V (1995) Support-vector network. Mach Learn 20:273–297

    MATH  Google Scholar 

  26. Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York

    Book  MATH  Google Scholar 

  27. Sanei S, Chambers JA (2007) EEG signal processing (ch. 2). Wiley, Chichester

    Google Scholar 

  28. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single trial eeg dynamics. J Neurosci Meth 134:9–21

    Article  Google Scholar 

  29. Chatrian GE, Bergamini L, Dondey M, Klass DW, Lennox-Buchthal M, Petersén I (1974) A glossary of terms most commonly used by clinical electroencephalographers. Electroenceph Clin Neurophysiol 37:538–548

    Article  Google Scholar 

  30. Townsend G, Lapallo BK, Boulay CB, Krusienski DJ, Frye GE, Hauser CK, Schwartz NE, Vaughan TM, Wolpaw JR, Sellers EW (2010) A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 121(7):1109–1120

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Science Foundation of China (NSFC) under Grant Nos. 90820305 and 60775040. The authors would like to thank all subjects for their participation.

Author information

Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Bei**g, 100084, China

    Zimu Zhang & Zhidong Deng

Authors
  1. Zimu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhidong Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zimu Zhang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Bei**g, People's Republic of China

    Fuchun Sun

  2. College of Mechatronics and Automation, National University of Defense Technolog, Changsha, People's Republic of China

    Dewen Hu

  3. Department of Computer Science and Techn, Tsinghua University, Bei**g, People's Republic of China

    Hua** Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, Z., Deng, Z. (2014). An Automatic SSVEP Component Selection Measure for High-Performance Brain-Computer Interface. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37835-5_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37834-8

  • Online ISBN: 978-3-642-37835-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation