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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
Article

Abstract

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.

Keywords

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

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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

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