Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials_Journal of Biomedical Engineering

Authors：

ZHAO Wei ¹ , XU Lichao ² , XIAO Xiaolin ^1,2 , YI Weibo ³ , CHEN Yuanfang ³ , WANG Kun ^1,2 ,  XU Minpeng ^1,2 , MING Dong ^1,2

1. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P. R. China;
2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China;
3. Beijing Institute of Mechanical Equipment, Beijing 100000, P. R. China;

Corresponding author：

XU Minpeng, Email: minpeng.xu@tju.edu.cn

Keywords：

Steady-state asymmetric visual evoked potentials; Phase modulation; Feature enhancement; Brain-computer interface

DOI：

10.7507/1001-5515.202208047

Video：

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Abstract Full text Figures/Tables Video References Cited by

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

Citation： ZHAO Wei, XU Lichao, XIAO Xiaolin, YI Weibo, CHEN Yuanfang, WANG Kun, XU Minpeng, MING Dong. Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials. Journal of Biomedical Engineering, 2023, 40(3): 409-417. doi: 10.7507/1001-5515.202208047 Copy

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2.	Regan D. Evoked potentials and evoked magnetic fields in science and medicine. New York: Elsevier, 1989.
3.	Saenz M, Buracas G T, Boynton G M. Global effects of feature-based attention in human visual cortex. Nat Neurosci, 2002, 5(7): 631-632.
4.	McMillan G, Calhoun G, Middendorf M, et al. Direct brain interface utilizing self-regulation of steady-state visual evoked response (SSVER)//RESNA ‘95 Annual Conference. Vancouver: Proc, 1995: 693.
5.	Middendorf M, McMillan G, Calhoun G, et al. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng, 2000, 8(2): 211-214.
6.	Fisher R S, Harding G, Erba G, et al. Photic- and pattern-induced seizures: a review for the Epilepsy Foundation of America Working Group. Epilepsia, 2005, 46(9): 1426-1441.
7.	Chen X, Zhao B, Wang Y, et al. Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm. J Neural Eng, 2019, 16(2): 026012.
8.	Volosyak I, Valbuena D, Lüth T, et al. BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?. IEEE Trans Neural Syst Rehabil Eng, 2011, 19(3): 232-239.
9.	Maye A, Zhang D, Engel A K. Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(7): 1026-1036.
10.	Chen J, Zhang D, Engel A K, et al. Application of a single-flicker online SSVEP BCI for spatial navigation. PLoS One, 2017, 12(5): e0178385.
11.	Yue L, Xiao X, Xu M, et al. A brain-computer interface based on high-frequency steady-state asymmetric visual evoked potentials//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 3090-3093.
12.	Wandell B A, Brewer A A, Dougherty R F. Visual field map clusters in human cortex. Philos Trans R Soc Lond B Biol Sci, 2005, 360(1456): 693-707.
13.	Wang Z, Hu H, Chen X, et al. A novel SSVEP-based brain-computer interface using joint frequency and space modulation//IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Toronto: IEEE, 2020: 906-911.
14.	Di Russo F, Pitzalis S, Aprile T, et al. Spatiotemporal analysis of the cortical sources of the steady‐state visual evoked potential. Hum Brain Mapp, 2007, 28(4): 323-334.
15.	Vanegas M I, Blangero A, Kelly S P. Exploiting individual primary visual cortex geometry to boost steady state visual evoked potentials. J Neural Eng, 2013, 10(3): 036003.
16.	Thaler L, Schütz A C, Goodale M A, et al. What is the best fixation target? The effect of target shape on stability of fixational eye movements. Vision Res, 2013, 76: 31-42.
17.	Towle V L, Bolaños J, Suarez D, et al. The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy. Electroencephalogr Clin Neurophysiol, 1993, 86(1): 1-6.
18.	Alotaiby T, El-Samie F E A, Alshebeili S A, et al. A review of channel selection algorithms for EEG signal processing. Eurasip J Adv Signal Process, 2015, 2015(1): 1-21.
19.	Regan D. Electrical responses evoked from the human brain. Sci Am, 1979, 241(6): 134-149.
20.	Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
21.	Müller-Putz G R, Scherer R, Brauneis C, et al. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng, 2005, 2(4): 123-130.
22.	Galloway N R. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Br J Ophthalmol, 1990, 74(4): 255.
23.	Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008.
24.	Bishop P O, Jeremy D, Lance J W. The optic nerve; properties of a central tract. J Physiol, 1953, 121(2): 415-432.
25.	Zhang N, Liu Y, Yin E, et al. Retinotopic and topographic analyses with gaze restriction for steady-state visual evoked potentials. Sci Rep, 2019, 9(1): 4472.
26.	Ming G, Wang Y, Pei W, et al. Characteristics of high-frequency SSVEPs evoked by visual stimuli at different polar angles//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 3031-3034.
27.	Amunts K, Malikovic A, Mohlberg H, et al. Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable?. Neuroimage, 2000, 11(1): 66-84.
28.	Wurtz R H, Kandel E R. Central visual pathways. Principles of neural science, 2000, 4: 523-545.
29.	Vialatte F B, Maurice M, Dauwels J, et al. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol, 2010, 90(4): 418-438.

1. Vidal J J. Toward direct brain-computer communication. Annu Rev Biomed Eng, 1973, 2(1): 157-180.
2. Regan D. Evoked potentials and evoked magnetic fields in science and medicine. New York: Elsevier, 1989.
3. Saenz M, Buracas G T, Boynton G M. Global effects of feature-based attention in human visual cortex. Nat Neurosci, 2002, 5(7): 631-632.
4. McMillan G, Calhoun G, Middendorf M, et al. Direct brain interface utilizing self-regulation of steady-state visual evoked response (SSVER)//RESNA ‘95 Annual Conference. Vancouver: Proc, 1995: 693.
5. Middendorf M, McMillan G, Calhoun G, et al. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng, 2000, 8(2): 211-214.
6. Fisher R S, Harding G, Erba G, et al. Photic- and pattern-induced seizures: a review for the Epilepsy Foundation of America Working Group. Epilepsia, 2005, 46(9): 1426-1441.
7. Chen X, Zhao B, Wang Y, et al. Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm. J Neural Eng, 2019, 16(2): 026012.
8. Volosyak I, Valbuena D, Lüth T, et al. BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?. IEEE Trans Neural Syst Rehabil Eng, 2011, 19(3): 232-239.
9. Maye A, Zhang D, Engel A K. Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(7): 1026-1036.
10. Chen J, Zhang D, Engel A K, et al. Application of a single-flicker online SSVEP BCI for spatial navigation. PLoS One, 2017, 12(5): e0178385.
11. Yue L, Xiao X, Xu M, et al. A brain-computer interface based on high-frequency steady-state asymmetric visual evoked potentials//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 3090-3093.
12. Wandell B A, Brewer A A, Dougherty R F. Visual field map clusters in human cortex. Philos Trans R Soc Lond B Biol Sci, 2005, 360(1456): 693-707.
13. Wang Z, Hu H, Chen X, et al. A novel SSVEP-based brain-computer interface using joint frequency and space modulation//IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Toronto: IEEE, 2020: 906-911.
14. Di Russo F, Pitzalis S, Aprile T, et al. Spatiotemporal analysis of the cortical sources of the steady‐state visual evoked potential. Hum Brain Mapp, 2007, 28(4): 323-334.
15. Vanegas M I, Blangero A, Kelly S P. Exploiting individual primary visual cortex geometry to boost steady state visual evoked potentials. J Neural Eng, 2013, 10(3): 036003.
16. Thaler L, Schütz A C, Goodale M A, et al. What is the best fixation target? The effect of target shape on stability of fixational eye movements. Vision Res, 2013, 76: 31-42.
17. Towle V L, Bolaños J, Suarez D, et al. The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy. Electroencephalogr Clin Neurophysiol, 1993, 86(1): 1-6.
18. Alotaiby T, El-Samie F E A, Alshebeili S A, et al. A review of channel selection algorithms for EEG signal processing. Eurasip J Adv Signal Process, 2015, 2015(1): 1-21.
19. Regan D. Electrical responses evoked from the human brain. Sci Am, 1979, 241(6): 134-149.
20. Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
21. Müller-Putz G R, Scherer R, Brauneis C, et al. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng, 2005, 2(4): 123-130.
22. Galloway N R. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Br J Ophthalmol, 1990, 74(4): 255.
23. Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008.
24. Bishop P O, Jeremy D, Lance J W. The optic nerve; properties of a central tract. J Physiol, 1953, 121(2): 415-432.
25. Zhang N, Liu Y, Yin E, et al. Retinotopic and topographic analyses with gaze restriction for steady-state visual evoked potentials. Sci Rep, 2019, 9(1): 4472.
26. Ming G, Wang Y, Pei W, et al. Characteristics of high-frequency SSVEPs evoked by visual stimuli at different polar angles//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 3031-3034.
27. Amunts K, Malikovic A, Mohlberg H, et al. Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable?. Neuroimage, 2000, 11(1): 66-84.
28. Wurtz R H, Kandel E R. Central visual pathways. Principles of neural science, 2000, 4: 523-545.
29. Vialatte F B, Maurice M, Dauwels J, et al. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol, 2010, 90(4): 418-438.

Journal of Biomedical Engineering

Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials

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