Research on the separability of steady-state visual evoked potential features modulated by different visual attentional states_Journal of Biomedical Engineering

Authors：

XU Minpeng ^1,2 , CHENG Xiumin ¹ ,  MING Dong ^1,2

1. Laboratory of Neural Engineering and Rehabilitation, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P.R.China;
2. Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China;

Corresponding author：

MING Dong, Email: richardming@tju.edu.cn

Keywords：

visual attentional states; steady-state visual evoked potential; discriminant canonical pattern matching algorithm; linear discrimination analysis algorithm; canonical correlation analysis algorithm

DOI：

10.7507/1001-5515.201811046

Video：

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Abstract Full text Figures/Tables Video References Cited by

Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.

Citation： XU Minpeng, CHENG Xiumin, MING Dong. Research on the separability of steady-state visual evoked potential features modulated by different visual attentional states. Journal of Biomedical Engineering, 2019, 36(5): 705-710. doi: 10.7507/1001-5515.201811046 Copy

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2.	Lee Y C, Lin W C, Cherng F Y, et al. A visual attention monitor based on Steady-State visual evoked potential. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(3): 399-408.
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4.	Debettencourt M T, Cohen J D, Lee R F, et al. Closed-loop training of attention with real-time brain imaging. Nat Neurosci, 2015, 18(3): 470-475.
5.	Awh E, Vogel E K. Attention: feedback focuses a wandering mind. Nat Neurosci, 2015, 18(3): 327-328.
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7.	Lee C H J, Jang C Y I, Chen T H D, et al. Attention meter: a vision-based input toolkit for interaction designers// CHI'06 Extended Abstracts on Human Factors in Computing Systems. Montréal: ACM, 2006: 1007-1012.
8.	Szafir D, Mutlu B. Pay attention!: designing adaptive agents that monitor and improve user engagement// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Austin: ACM, 2012: 11-20.
9.	Barry R J, Clarke A R, Johnstone S J, et al. Electroencephalogram θ/β ratio and arousal in attention-deficit/hyperactivity disorder: Evidence of independent processes. Biol Psychiatry, 2009, 66(4): 398-401.
10.	Liao L D, Chen C Y, Wang I J, et al. Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil, 2012, 9(1): 1-12.
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12.	Weissman D H, Roberts K C, Visscher K M, et al. The neural bases of momentary lapses in attention. Nat Neurosci, 2006, 9(7): 971-978.
13.	Chen D, Vertegaal R. Using mental load for managing interruptions in physiologically attentive user interfaces// CHI'04 Extended Abstracts on Human Factors in Computing Systems. Vienna: ACM, 2004: 1513-1516.
14.	Morgan S T, Hansen J C, Hillyard S A. Selective attention to stimulus location modulates the steady-state visual evoked potential. Proc Natl Acad Sci U S A, 1996, 93(10): 4770-4774.
15.	Saupe K, Schröeger E, Andersen S K, et al. Neural mechanisms of intermodal sustained selective attention with concurrently presented auditory and visual stimuli. Front Hum Neurosci, 2009, 3: 1-13.
16.	吴正华, 尧德中. 用稳态视觉诱发电位研究注意的选择机制. 生物物理学报, 2006, 22(6): 455-460.
17.	Müller M M, Andersen S, Trujillo N J, et al. Feature-selective attention enhances color signals in early visual areas of the human brain. Proc Natl Acad Sci U S A, 2006, 103(38): 14250-14254.
18.	Kelly S P, Lalor E C, Reilly R B, et al. Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Trans Neural Syst Rehabil Eng, 2005, 13(2): 172-178.
19.	Ortner R, Guger C, Prueckl R, et al. SSVEP based brain-computer interface for robot control// International Conference on Computers for Handicapped Persons. Heidelberg: Springer, 2010: 85-90.
20.	Nakanishi M, Wang Y, Wang Y T, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst, 2014, 24(6): 1450019.
21.	Xu Minpeng, Xiao Xiaolin, Wang Yijun, et al. A brain-computer Interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng, 2018, 65(5): 1166-1175.
22.	孙长城. 基于三维编码刺激序列的视觉 P300-Speller 诱发 ERP 研究. 天津: 天津大学, 2012.
23.	Verbruggen F, Logan G D. Models of response inhibition in the stop-signal and stop-change paradigms. Neurosci Biobehav Rev, 2009, 33(5): 647-661.

1. Anderson J R. Cognitive psychology and its implications. 6th ed. Cotswolds: Worth Publishers, 2004: 519.
2. Lee Y C, Lin W C, Cherng F Y, et al. A visual attention monitor based on Steady-State visual evoked potential. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(3): 399-408.
3. Chun M M, Golomb J D, Turk-Browne N B. A taxonomy of external and internal attention. Annu Rev Psychol, 2011, 62(1): 73-101.
4. Debettencourt M T, Cohen J D, Lee R F, et al. Closed-loop training of attention with real-time brain imaging. Nat Neurosci, 2015, 18(3): 470-475.
5. Awh E, Vogel E K. Attention: feedback focuses a wandering mind. Nat Neurosci, 2015, 18(3): 327-328.
6. Shell J S, Vertegaal R, Skaburskis A W. EyePliances: attention-seeking devices that respond to visual attention// CHI'03 Extended Abstracts on Human Factors in Computing Systems. Lauderdale: ACM, 2003: 770-771.
7. Lee C H J, Jang C Y I, Chen T H D, et al. Attention meter: a vision-based input toolkit for interaction designers// CHI'06 Extended Abstracts on Human Factors in Computing Systems. Montréal: ACM, 2006: 1007-1012.
8. Szafir D, Mutlu B. Pay attention!: designing adaptive agents that monitor and improve user engagement// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Austin: ACM, 2012: 11-20.
9. Barry R J, Clarke A R, Johnstone S J, et al. Electroencephalogram θ/β ratio and arousal in attention-deficit/hyperactivity disorder: Evidence of independent processes. Biol Psychiatry, 2009, 66(4): 398-401.
10. Liao L D, Chen C Y, Wang I J, et al. Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil, 2012, 9(1): 1-12.
11. Rosenberg M D, Finn E S, Scheinost D, et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci, 2016, 19(1): 165-171.
12. Weissman D H, Roberts K C, Visscher K M, et al. The neural bases of momentary lapses in attention. Nat Neurosci, 2006, 9(7): 971-978.
13. Chen D, Vertegaal R. Using mental load for managing interruptions in physiologically attentive user interfaces// CHI'04 Extended Abstracts on Human Factors in Computing Systems. Vienna: ACM, 2004: 1513-1516.
14. Morgan S T, Hansen J C, Hillyard S A. Selective attention to stimulus location modulates the steady-state visual evoked potential. Proc Natl Acad Sci U S A, 1996, 93(10): 4770-4774.
15. Saupe K, Schröeger E, Andersen S K, et al. Neural mechanisms of intermodal sustained selective attention with concurrently presented auditory and visual stimuli. Front Hum Neurosci, 2009, 3: 1-13.
16. 吴正华, 尧德中. 用稳态视觉诱发电位研究注意的选择机制. 生物物理学报, 2006, 22(6): 455-460.
17. Müller M M, Andersen S, Trujillo N J, et al. Feature-selective attention enhances color signals in early visual areas of the human brain. Proc Natl Acad Sci U S A, 2006, 103(38): 14250-14254.
18. Kelly S P, Lalor E C, Reilly R B, et al. Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication. IEEE Trans Neural Syst Rehabil Eng, 2005, 13(2): 172-178.
19. Ortner R, Guger C, Prueckl R, et al. SSVEP based brain-computer interface for robot control// International Conference on Computers for Handicapped Persons. Heidelberg: Springer, 2010: 85-90.
20. Nakanishi M, Wang Y, Wang Y T, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst, 2014, 24(6): 1450019.
21. Xu Minpeng, Xiao Xiaolin, Wang Yijun, et al. A brain-computer Interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng, 2018, 65(5): 1166-1175.
22. 孙长城. 基于三维编码刺激序列的视觉 P300-Speller 诱发 ERP 研究. 天津: 天津大学, 2012.
23. Verbruggen F, Logan G D. Models of response inhibition in the stop-signal and stop-change paradigms. Neurosci Biobehav Rev, 2009, 33(5): 647-661.

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