Multimedia Tools and Applications

, Volume 78, Issue 10, pp 12865–12882 | Cite as

Bispectral analysis-based approach for steady-state visual evoked potentials detection

  • Omar TriguiEmail author
  • Wassim Zouch
  • Mohamed Ben Slima
  • Mohamed Ben Messaoud


Brain-Computer Interface (BCI) systems are widely based on steady-state visual evoked potentials (SSVEP) detection using electroencephalography (EEG) signals. SSVEP-based BCIs are becoming attractive due to their higher signal-to-noise ratio (SNR) as well as faster information transfer rate (ITR). However, their performances are largely affected by the interference coming from the spontaneous EEG activities which intrinsically restrict their efficiency in distinguishing between SSVEPs and background EEG activities. In this paper, we introduce a new approach for the detection of SSVEP based on bispectral analysis to palliate the frequency-dependent bias. A COMB filter associated with a wavelet denoising filter is firstly used to minimize the noise while improving the SNR of phase signals. Next, the complementary orthogonal projections and the principle component analysis (PCA) are used to decompose the components related to SSVEPs and components related to brain activities. Finally, the bispectrum, a powerful tool for the analysis and the characterization of nonlinear properties of stochastic signals, is used to extract the features of the EEG signal benefiting from the information about the phase coupling of the signal components. The results of experiments, using two databases on five (or ten) subjects, show that the proposed approach significantly outperformed the standard CCA approach in distinguishing the target frequency and in average information transfer rate.


Brain-Computer Interface Steady State Visual Evoked Potential COMB filter Spatial filter Bi-spectral analysis 


  1. 1.
    Ammar S, Trigui O, Senouci M (2017) Automated patient-specific seizure detection system with self-parameters adaptation. Control Intell Syst 45(4):29–39Google Scholar
  2. 2.
    Bin G, Gao X, Yan Z, Hong B, Gao S (2009) An online multichannel SSVEP-based brain computer interface using a canonical correlation analysis method. J Neural Eng 6Google Scholar
  3. 3.
    Chang C, Lee P, Lin E (2017) Variable delay digital comb filter extraction of weak phase signals for SSVEP. Biomed Signal Process Control 31:211–216CrossRefGoogle Scholar
  4. 4.
    Chella F, D'Andrea A, Basti A, Pizzella V, Marzetti L (2017) Non-linear analysis of scalp EEG by using bispectra: the effect of the reference choice. Front Neurosci 11:1–15CrossRefGoogle Scholar
  5. 5.
    Dongxue L, Too Chuan TJ, Chi Z, Feng D (2015) Design of an online BCI system based on CCA detection method. Chinese control conference, Hangzhou, China, pp 4728–4733Google Scholar
  6. 6.
    Friman O, Volosyak I, Gräser A (2007) Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans Biomed Eng 54(4):742–750CrossRefGoogle Scholar
  7. 7.
    Hairston W-D, Whitaker K-W, Ries A-J, Vettel J-M, Bradford J-C, Kerick S-E, McDowell K (2014) Usability of four commercially-oriented EEG systems. J Neural Eng 11Google Scholar
  8. 8.
    Huang R, Heng F, Hu B, Peng H, Zhao Q, Shi Q, Han J (2014) Artifacts reduction method in EEG signals with wavelet transform and adaptive filter. In: Ślȩzak D, Tan AH, Peters JF, Schwabe L (eds) Brain informatics and health. Lecture notes in computer science. Springer, Cham, pp 122–131CrossRefGoogle Scholar
  9. 9.
    Hwang H-J, Han C-H, Lim J-H, Kim Y-W, Choi S-I, An K-O, Lee J-H, Cha H-S, Hyun Kim S, Im C-H (2017) Clinical feasibility of brain-computer interface based on steady-state visual evoked potential in patients with locked-in syndrome: case studies. Psychophysiology 54(3):444–451CrossRefGoogle Scholar
  10. 10.
    Joy Martis R, Rajendra Acharya U, Mandana K-M, Ray A-K, Chakraborty C (2013) Cardiac decision making using higher order spectra. Biomed Signal Process Control 8(2):193–203CrossRefGoogle Scholar
  11. 11.
    Kolodziej M, Majkowski A, Rak R-J (2015) A new method of spatial filters design for brain-computer interface based on steady state visually evoked potentials. International conference on intelligent data acquisition and advanced computing systems: technology and applications, Warsaw, Poland, pp 697–700Google Scholar
  12. 12.
    Li Y, Zhou G, Graham D, Holtzhauer A (2015) Towards an EEG-based brain-computer interface for online robot control. Multimedia Tools and Applications 75(13):7999–8017CrossRefGoogle Scholar
  13. 13.
    Lin Z, Zhang C, Wu W, Gao X (2007) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 54(6):1172–1176CrossRefGoogle Scholar
  14. 14.
    Liu Q, Chen K, Ai Q, Xie S-Q (2014) Review: recent development of signal processing algorithms for SSVEP-based brain computer interfaces. J Med Biol Eng 34(4):299–309CrossRefGoogle Scholar
  15. 15.
    Mamun M, Al-Kadi M, Marufuzzaman M (2013) Effectiveness of wavelet denoising on electroencephalogram signals. J Appl Res Technol 11(1):156–160CrossRefGoogle Scholar
  16. 16.
    Martišius I, Damaševičius R (2016) A prototype SSVEP based real time BCI gaming system. Comput Intell Neurosci 2016:1–15CrossRefGoogle Scholar
  17. 17.
    Materka A, Byczuk M (2006) Using comb filter to enhance SSVEP for BCI applications. 3rd international conference on advances in medical, signal and information processing. Glasgow, UKGoogle Scholar
  18. 18.
    Nakanishi M, Wang Y, Wang Y-T, Jung T-P (2015) A comparison study of canonical correlation analysis based methods for setecting steady-state visual evoked potentials. PLoS One 10(10)Google Scholar
  19. 19.
    Nataraj S-K, Paulraj M-P, Bin Yaacob S, Adom A-H (2015) Performance comparison of TEP and VEP responses using bispectral estimation to command an intelligent robot chair with communication aid. Indian J Sci Technol 8(20):1–11CrossRefGoogle Scholar
  20. 20.
    Ortner R, Allison B-Z, Korisek G, Gaggl H, Pfurtscheller G (2011) An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans Neural Syst Rehabil Eng 19(1):1–5CrossRefGoogle Scholar
  21. 21.
    Shang-Ming Z, Gan J-Q, Sepulveda F (2008) Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Inf Sci 178(6):1629–1640CrossRefGoogle Scholar
  22. 22.
    Shen H, Zhao L, Bian Y, Xiao L (2009) Research on SSVEP-based controlling system of multi-DoF manipulator. In: Yu W, He H, Zhang N (eds) Advances in neural networks. Lecture notes in computer science. Springer, Berlin, pp 171–177Google Scholar
  23. 23.
    Sigl J-C, Chamoun N-G (1994) An introduction to bispectral analysis for the electroencephalogram. J Clin Monit 10(6):392–404CrossRefGoogle Scholar
  24. 24.
    Stamps K, Hamam Y (2010) Towards inexpensive BCI control for wheelchair navigation in the enabled environment – a hardware survey. In: Yao Y, Sun R, Poggio T, Liu J, Zhong N, Huang J (eds) Brain informatics. Lecture notes in computer science. Springer, Berlin, pp 336–345Google Scholar
  25. 25.
    Sun G, Yang Y, Leng Y, Wang H, Ge S (2017) The distribution of classification accuracy over the whole head for a steady state visual evoked potential based brain-computer interface. Procedia Computer Science 107:389–394CrossRefGoogle Scholar
  26. 26.
    Trigui O, Zouch W, Ben Messaoud M (2017) Hilbert-Huang transform and Welch's method for motor imagery based brain computer interface. International Journal of Cognitive Informatics and Natural Intelligence 11(3):48–68CrossRefGoogle Scholar
  27. 27.
    Wang Y, Zhang Z, Gao X, Gao S (2004) Lead selection for SSVEP based brain-computer interface. International conference of the IEEE engineering in medicine and biology society. San Francisco, CA, USA, pp 4507–4510Google Scholar
  28. 28.
    Wolpaw J-R, Ramoser H, McFarland D-J, Pfurtscheller G (1998) EEG-based communication: improved accuracy by response verification. IEEE Trans Rehabil Eng 6(3):326–333CrossRefGoogle Scholar
  29. 29.
    Xie S, Meng W (2017) Signal processing methods for SSVEP-based BCIs. In: Biomechatronics in medical rehabilitation. Springer, Cham, pp 51–68CrossRefGoogle Scholar
  30. 30.
    Zhao L, Yuan P , Xiao L, Meng Q, Hu D, Shen H (2010) Research on SSVEP feature extraction based on HHT. International conference on fuzzy systems and knowledge discovery, Yantai, China, pp 2220–2223Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Advanced Technologies for Medicine and Signals ‘ATMS’ENIS, Sfax UniversitySfaxTunisia
  2. 2.King Abdulaziz University (KAU)JeddahSaudi Arabia

Personalised recommendations