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.
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Ammar S, Trigui O, Senouci M (2017) Automated patient-specific seizure detection system with self-parameters adaptation. Control Intell Syst 45(4):29–39
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 6
Chang C, Lee P, Lin E (2017) Variable delay digital comb filter extraction of weak phase signals for SSVEP. Biomed Signal Process Control 31:211–216
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 11:1–15
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 4728–4733
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):742–750
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 11
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 122–131
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):444–451
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):193–203
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 697–700
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):7999–8017
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):1172–1176
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):299–309
Mamun M, Al-Kadi M, Marufuzzaman M (2013) Effectiveness of wavelet denoising on electroencephalogram signals. J Appl Res Technol 11(1):156–160
Martišius I, Damaševičius R (2016) A prototype SSVEP based real time BCI gaming system. Comput Intell Neurosci 2016:1–15
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, UK
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)
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):1–11
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):1–5
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):1629–1640
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 171–177
Sigl J-C, Chamoun N-G (1994) An introduction to bispectral analysis for the electroencephalogram. J Clin Monit 10(6):392–404
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 336–345
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 107:389–394
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):48–68
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 4507–4510
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):326–333
Xie S, Meng W (2017) Signal processing methods for SSVEP-based BCIs. In: Biomechatronics in medical rehabilitation. Springer, Cham, pp 51–68
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 2220–2223
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Trigui, O., Zouch, W., Ben Slima, M. et al. Bispectral analysis-based approach for steady-state visual evoked potentials detection. Multimed Tools Appl 78, 12865–12882 (2019). https://doi.org/10.1007/s11042-018-6029-y
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DOI: https://doi.org/10.1007/s11042-018-6029-y