Signal Processing Methods for SSVEP-Based BCIs

  • Xing Song
  • Shane XieEmail author
  • Wei Meng


Frequency coded SSVEP-based BCIs have been increasingly studied in recent years. They have shown potential as useful tools for disabled people to restore fundamental skills of communication and control. Distinguishing target frequency components from weak and noisy SSVEPs with high accuracy using a minimum of recording electrodes is one of the key issues for a practical SSVEP-based BCI. The most challenging task is to effectively eliminate the artefacts whose frequency spectra usually overlap with those of the target signals. In this chapter, a new signal processing method based on the adjacent narrow band filter (ANBF) is proposed for the purpose of artefact reduction and frequency recognition in a 12-class SSVEP-based BCI. The proposed ANBF method effectively suppresses irrelevant artefacts whose frequency spectra overlap with those of the targets, and successfully estimates the noise-free energy of the target frequency bands. The proposed ANBF is compared with the widely used Canonical Correlation Analysis (CCA) and verified online with two channel EEG data from nine healthy participants. This study was done without preventing participants’ normal eye blinks and high performance can be achieved with no more than two electrodes, the proposed ANBF provides a new approach for SSVEP-based BCIs for real-life use.


  1. 1.
    Birbaumer, N., et al., A spelling device for the paralysed. Nature, 1999. 398(6725): p. 297–298.Google Scholar
  2. 2.
    Hwang, H.-J., K. Kwon, and C.-H. Im, Neurofeedback-based motor imagery training for brain-computer interface (BCI). Journal of Neuroscience Methods, 2009. 179(1): p. 150–156.Google Scholar
  3. 3.
    Cecotti, H., Spelling with non-invasive brain–computer interfaces – Current and future trends. Journal of Physiology-Paris, 2011. 105(1–3): p. 106–114.Google Scholar
  4. 4.
    Herrmann, C., Human EEG responses to 1–100 Hz flicker: Resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Experimental Brain Research, 2001. 137: p. 346–353.Google Scholar
  5. 5.
    Gratton, G., Dealing with artifacts: The EOG contamination of the event-related brain potential. Behavior Research Methods, 1998. 30(1): p. 44–53.Google Scholar
  6. 6.
    Teplan, M., Fundamentals of EEG measurement. Measurement Science Review, 2002. 2(2): p. 1–11.Google Scholar
  7. 7.
    Fatourechi, M., et al., EMG and EOG artifacts in brain computer interface systems: A survey. Clinical Neurophysiology, 2007. 118(3): p. 480–494.Google Scholar
  8. 8.
    McFarland, D.J., et al., Brain-computer interface (BCI) operation: Signal and noise during early training sessions. Clinical Neurophysiology, 2005. 116(1): p. 56–62.Google Scholar
  9. 9.
    Ochoa, C.J. and J. Polich, P300 and blink instructions. Clinical Neurophysiology, 2000. 111(1): p. 93–98.Google Scholar
  10. 10.
    Verleger, R., The instruction to refrain from blinking affects auditory P3 and N1 amplitudes. Electroencephalography and Clinical Neurophysiology, 1991. 78(3): p. 240–251.Google Scholar
  11. 11.
    An, L. and J.S. Thomas, A user-friendly SSVEP-based brain–computer interface using a time-domain classifier. Journal of Neural Engineering, 2010. 7(2): p. 026010.Google Scholar
  12. 12.
    Croft, R.J. and R.J. Barry, Removal of ocular artifact from the EEG: A review. Neurophysiologie Clinique-Clinical Neurophysiology, 2000. 30(1): p. 5–19.Google Scholar
  13. 13.
    Gupta, S. and H. Singh. Preprocessing EEG signals for direct human-system interface. in IEEE International Joint Symposia on Intelligence and Systems, 1996.Google Scholar
  14. 14.
    McFarland, D.J., et al., Spatial filter selection for EEG-based communication. Electroencephalography and Clinical Neurophysiology, 1997. 103(3): p. 386–394.Google Scholar
  15. 15.
    Manoilov, P., Eye-blinking artefacts analysis, in International Conference on Computer Systems and Technologies, 2007. Bulgaria. p. 1–6.Google Scholar
  16. 16.
    Hoffmann, S. and M. Falkenstein, The correction of eye blink artefacts in the EEG: A comparison of two prominent methods. Plos One, 2008. 3(8): p. e3004.Google Scholar
  17. 17.
    Schlögl, A., et al., A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology, 2007. 118(1): p. 98–104.Google Scholar
  18. 18.
    Anwar, H., et al., Automatic removal of ocular artifacts in the EEG without an EOG reference channel. 7th Nordic Signal Processing Symposium, 2006. p. 130–133.Google Scholar
  19. 19.
    Joyce, C.A., I.F. Gorodnitsky, and M. Kutas, Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology, 2004. 41(2): p. 313–325.Google Scholar
  20. 20.
    Barbati, G., et al., Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. Clinical Neurophysiology, 2004. 115(5): p. 1220–1232.Google Scholar
  21. 21.
    Choi, S., et al., Blind source separation and independent component analysis: A Review. Neural Information Processing-Letters and Reviews, 2005. 6(1): p. 1–57.Google Scholar
  22. 22.
    Zhou, W.J., Removing eye-movement artifacts from the EEG during the intracarotid amobarbital procedure. Epilepsia (Series 4), 2005. 46(3): p. 409–414.Google Scholar
  23. 23.
    Friman, O., I. Volosyak, and A. Graser, Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Transactions on Biomedical Engineering, 2007. 54(4): p. 742–750.Google Scholar
  24. 24.
    Berg, P. and M. Scherg, A multiple source approach to the correction of eye artifacts. Electroencephalography and Clinical Neurophysiology, 1994. 90(3): p. 229–241.Google Scholar
  25. 25.
    Lagerlund, T.D., F.W. Sharbrough, and N.E. Busacker, Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Journal of Clinical Neurophysiology, 1997. 14(1): p. 73–82.Google Scholar
  26. 26.
    Selvan, S. and R. Srinivasan, Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique. Signal Processing Letters, 1999. 6(12): p. 330–332.Google Scholar
  27. 27.
    Zhan, D.Q., et al., Wavelet denoising and optimization of two-dimensional correlation IR spectroscopy. Spectroscopy and Spectral Analysis, 2004. 24(12): p. 1549–1552.Google Scholar
  28. 28.
    Materka, A. and M. Byczuk, Alternate half-field stimulation technique for SSVEP-based brain-computer interfaces. Electronics Letters, 2006. 42(6): p. 321–322.Google Scholar
  29. 29.
    Wu, C.H., et al., Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing. Journal of Neuroscience Methods, 2011. 196(1): p. 170–181.Google Scholar
  30. 30.
    Pham, T.T.H., et al., A test of four EOG correction methods using an improved validation technique. International Journal of Psychophysiology, 2011. 79(2): p. 203–210.Google Scholar
  31. 31.
    Lalor, E., et al., Steady-State VEP-based brain-computer interface control in an immersive 3D gaming environment. Journal on Advances in Signal Processing, 2005. 2005(19): p. 706906.Google Scholar
  32. 32.
    Müller-Putz, G.R., et al., Steady-state visual evoked potential (SSVEP)-based communication: Impact of harmonic frequency components. Journal of Neural Engineering, 2005. 2(4): p. 123–130.Google Scholar
  33. 33.
    Shyu, K.-K., et al., Dual-frequency steady-state visual evoked potential for brain computer interface. Neuroscience Letters, 2010. 483(1): p. 28–31.Google Scholar
  34. 34.
    Kelly, S.P., et al., Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005. 13(2): p. 172–178.Google Scholar
  35. 35.
    Lin, Z., et al., Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Transactions on Biomedical Engineering, 2007. 54(6): p. 1172 – 1176.Google Scholar
  36. 36.
    Guangyu, B., et al., An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. Journal of Neural Engineering, 2009. 6(4): p. 046002.Google Scholar
  37. 37.
    Zhenghua, W. and Y. Dezhong, Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs. Journal of Neural Engineering, 2008. 5(1): p. 36.Google Scholar
  38. 38.
    Zhang, Y., et al., LASSO based stimulus frequency recognition model for SSVEP BCIs. Biomedical Signal Processing and Control, 2012. 7(2): p. 104–111.Google Scholar
  39. 39.
    Wenya, N., et al. A comparison of minimum energy combination and canonical correlation analysis for SSVEP detection. in 5th International IEEE/EMBS Conference on Neural Engineering, 2011.Google Scholar
  40. 40.
    Huang, N.E., Computing frequency by using generalized zero-crossing applied to intrinsic mode functions, 2006.Google Scholar
  41. 41.
    Garrett, D., et al., Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003. 11(2): p. 141–144.Google Scholar
  42. 42.
    Oldfield, R.C., Assessment and analysis of handedness - Edinburgh Inventory. Neuropsychologia, 1971. 9(1): p. 97–113.Google Scholar
  43. 43.
    Morgan, S.T., J.C. Hansen, and S.A. Hillyard, Selective attention to stimulus location modulates the steady-state visual evoked potential. Proceedings of the National Academy of Sciences of the United States of America, 1996. 10(93): p. 4770–4.Google Scholar
  44. 44.
    LaBerge, D., Attentional Processing—The Brain’s Art of Mindfulness. 1995, Cambridge, MA: Harvard University Press.Google Scholar
  45. 45.
    Nikulin, V.V., et al., A novel mechanism for evoked responses in the human brain. European Journal of Neuroscience, 2007. 25(10): p. 3146–3154.Google Scholar
  46. 46.
    Moratti, S., et al., Neural mechanisms of evoked oscillations: Stability and interaction with transient events. Human Brain Mapping, 2007. 28(12): p. 1318–1333.Google Scholar
  47. 47.
    Klimesch, W., et al., Event-related phase reorganization may explain evoked neural dynamics. Neuroscience & Biobehavioral Reviews, 2007. 31(7): p. 1003–1016.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversity of LeedsLeedsUnited Kingdom
  2. 2.Department of Mechanical EngineeringThe University of AucklandAucklandNew Zealand
  3. 3.School of Information EngineeringWuhan University of TechnologyWuhanChina
  4. 4.The University of AucklandAucklandNew Zealand

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