Recognition of high-frequency steady-state visual evoked potential for brain-computer interface_Journal of Biomedical Engineering

Authors：

LUO Ruixin ¹ , DOU Xinyi ² , XIAO Xiaolin ^1,2 , WU Qiaoyi ¹ ,  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;

Corresponding author：

XU Minpeng, Email: xmp52637@tju.edu.cn

Keywords：

Brain-computer interface; Steady-state visual evoked potential; High-frequency; Decoding algorithm

DOI：

10.7507/1001-5515.202302034

Video：

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Abstract Full text Figures/Tables Video References Cited by

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.

Citation： LUO Ruixin, DOU Xinyi, XIAO Xiaolin, WU Qiaoyi, XU Minpeng, MING Dong. Recognition of high-frequency steady-state visual evoked potential for brain-computer interface. Journal of Biomedical Engineering, 2023, 40(4): 683-691. doi: 10.7507/1001-5515.202302034 Copy

1.	Meng J, Xu M, Wang K, et al. Separable EEG features induced by timing prediction for active brain-computer interfaces. Sensors, 2020, 20(12): 3588.
2.	Xu M, He F, Jung T P, et al. Current challenges for the practical application of electroencephalography based brain-computer interfaces. Engineering, 2021, 7(12): 1710-1712.
3.	Wang Y, Wang R, Gao X, et al. A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2006, 14(2): 234-239.
4.	邵星翰. 基于 SSVEP 的脑-机接口信号处理方法研究. 山东大学, 2020.
5.	Jiang J, Yin E, Wang C, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng, 2018, 15(4): 046025.
6.	Pinheiro C G, Naves E L, Pino P, et al. Alternative communication systems for people with severe motor disabilities: a survey. Biomed Eng Online, 2011, 10: 31.
7.	Diez P F, Mut V A, Laciar E, et al. Mobile robot navigation with a self-paced brain-computer interface based on high-frequency SSVEP. Robotica, 2013, 32(5): 695-709.
8.	Won D O, Zhang H H, Guan C, et al. A BCI speller based on SSVEP using high frequency stimuli design//2014 IEEE international conference on systems, man, and cybernetics (SMC), San Diego: IEEE, 2014: 1068-1071.
9.	Diez P F, Torres Müller S M, Mut V A, et al. Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface. Med Eng Phys, 2013, 35(8): 1155-1164.
10.	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.
11.	Ye X, Yang C, Chen Y, et al. Multi-symbol time division coding for high-frequency steady-state visual evoked potential-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1693-1704.
12.	Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng, 2002, 49(10): 1181-1186.
13.	Bin G, Gao X, Yan Z, et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng, 2009, 6(4): 046002.
14.	Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci USA, 2015, 112(44): E6058-E6067.
15.	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.
16.	Wong C M, Wan F, Wang B, et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J Neural Eng, 2020, 17(1): 016026.
17.	Wong C M, Wang B, Wang Z, et al. Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements. IEEE Trans Biomed Eng, 2020, 67(11): 3057-3072.
18.	Sun Q, Chen M, Zhang L, et al. Similarity-constrained task-related component analysis for enhancing SSVEP detection. J Neural Eng, 2021, 18(4): 046080.
19.	Liu B, Chen X, Shi N, et al. Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1998-2007.
20.	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.
21.	Pan J, Gao X, Duan F, et al. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis. J Neural Eng, 2011, 8(3): 036027.
22.	Liang L, Yang C, Wang Y, et al. High-frequency SSVEP stimulation paradigm based on dual frequency modulation//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin: IEEE, 2019: 6184-6187.
23.	Liu W, Zhang L, Li C. A method for recognizing high-frequency steady-state visual evoked potential based on empirical modal decomposition and canonical correlation analysis//2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu: IEEE, 2019: 774-778.
24.	Ming G, Pei W, Chen H, et al. Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs. J Neural Eng, 2021, 18(5): 056046.
25.	许敏鹏, 吴乔逸, 熊文田, 等. 面向中高频 SSVEP 脑机接口的编解码算法研究. 信号处理, 2022, 38(9): 1881-1891.
26.	Jiang L, Pei W, Wang Y. A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60 Hz. China Commun, 2022, 19(2): 1-14.
27.	Vahid F, Behboodi M, Mahnam A. Bichromatic visual stimulus with subharmonic response to achieve a high-accuracy SSVEP BCI system with low eye irritation. Biomed Signal Proces, 2023, 83: 104629.
28.	Ming G, Pei W, Gao X, et al. A high-performance SSVEP-based BCI using imperceptible flickers. J Neural Eng, 2023, 20(1): 016042.
29.	Chen X, Liu B, Wang Y, et al. Optimizing stimulus frequency ranges for building a high-rate high frequency SSVEP-BCI. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 1277-1286.
30.	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 and Biology Society (EMBC), Montreal: IEEE, 2020: 3090-3093.
31.	Xu M, Xiao X, Wang Y, 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.

1. Meng J, Xu M, Wang K, et al. Separable EEG features induced by timing prediction for active brain-computer interfaces. Sensors, 2020, 20(12): 3588.
2. Xu M, He F, Jung T P, et al. Current challenges for the practical application of electroencephalography based brain-computer interfaces. Engineering, 2021, 7(12): 1710-1712.
3. Wang Y, Wang R, Gao X, et al. A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2006, 14(2): 234-239.
4. 邵星翰. 基于 SSVEP 的脑-机接口信号处理方法研究. 山东大学, 2020.
5. Jiang J, Yin E, Wang C, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng, 2018, 15(4): 046025.
6. Pinheiro C G, Naves E L, Pino P, et al. Alternative communication systems for people with severe motor disabilities: a survey. Biomed Eng Online, 2011, 10: 31.
7. Diez P F, Mut V A, Laciar E, et al. Mobile robot navigation with a self-paced brain-computer interface based on high-frequency SSVEP. Robotica, 2013, 32(5): 695-709.
8. Won D O, Zhang H H, Guan C, et al. A BCI speller based on SSVEP using high frequency stimuli design//2014 IEEE international conference on systems, man, and cybernetics (SMC), San Diego: IEEE, 2014: 1068-1071.
9. Diez P F, Torres Müller S M, Mut V A, et al. Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface. Med Eng Phys, 2013, 35(8): 1155-1164.
10. 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.
11. Ye X, Yang C, Chen Y, et al. Multi-symbol time division coding for high-frequency steady-state visual evoked potential-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1693-1704.
12. Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng, 2002, 49(10): 1181-1186.
13. Bin G, Gao X, Yan Z, et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng, 2009, 6(4): 046002.
14. Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci USA, 2015, 112(44): E6058-E6067.
15. 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.
16. Wong C M, Wan F, Wang B, et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J Neural Eng, 2020, 17(1): 016026.
17. Wong C M, Wang B, Wang Z, et al. Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements. IEEE Trans Biomed Eng, 2020, 67(11): 3057-3072.
18. Sun Q, Chen M, Zhang L, et al. Similarity-constrained task-related component analysis for enhancing SSVEP detection. J Neural Eng, 2021, 18(4): 046080.
19. Liu B, Chen X, Shi N, et al. Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1998-2007.
20. 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.
21. Pan J, Gao X, Duan F, et al. Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis. J Neural Eng, 2011, 8(3): 036027.
22. Liang L, Yang C, Wang Y, et al. High-frequency SSVEP stimulation paradigm based on dual frequency modulation//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin: IEEE, 2019: 6184-6187.
23. Liu W, Zhang L, Li C. A method for recognizing high-frequency steady-state visual evoked potential based on empirical modal decomposition and canonical correlation analysis//2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu: IEEE, 2019: 774-778.
24. Ming G, Pei W, Chen H, et al. Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs. J Neural Eng, 2021, 18(5): 056046.
25. 许敏鹏, 吴乔逸, 熊文田, 等. 面向中高频 SSVEP 脑机接口的编解码算法研究. 信号处理, 2022, 38(9): 1881-1891.
26. Jiang L, Pei W, Wang Y. A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60 Hz. China Commun, 2022, 19(2): 1-14.
27. Vahid F, Behboodi M, Mahnam A. Bichromatic visual stimulus with subharmonic response to achieve a high-accuracy SSVEP BCI system with low eye irritation. Biomed Signal Proces, 2023, 83: 104629.
28. Ming G, Pei W, Gao X, et al. A high-performance SSVEP-based BCI using imperceptible flickers. J Neural Eng, 2023, 20(1): 016042.
29. Chen X, Liu B, Wang Y, et al. Optimizing stimulus frequency ranges for building a high-rate high frequency SSVEP-BCI. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 1277-1286.
30. 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 and Biology Society (EMBC), Montreal: IEEE, 2020: 3090-3093.
31. Xu M, Xiao X, Wang Y, 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.

Journal of Biomedical Engineering

Recognition of high-frequency steady-state visual evoked potential for brain-computer interface

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