Advances in brain-computer interface based on high-frequency steady-state visual evoked potential_Journal of Biomedical Engineering

Authors：

ZHENG Chenguang ^1,2,3 , LIU Yang ¹ , XIAO Xiaolin ^1,2 , ZHOU Xiaoyu ² , XU Fangzhou ⁴ ,  XU Minpeng ^1,2 , MING Dong ^1,2

1. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China;
2. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China;
3. Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072, P. R. China;
4. School of Electronic and Information Engineering, Qilu University of Technology, Jinan 250353, P. R. China;

Corresponding author：

Keywords：

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

DOI：

10.7507/1001-5515.202205090

Video：

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Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

Citation： ZHENG Chenguang, LIU Yang, XIAO Xiaolin, ZHOU Xiaoyu, XU Fangzhou, XU Minpeng, MING Dong. Advances in brain-computer interface based on high-frequency steady-state visual evoked potential. Journal of Biomedical Engineering, 2023, 40(1): 155-162. doi: 10.7507/1001-5515.202205090 Copy

1.	Zhang Yangsong, Yin Erwei, Li Fali, et al. Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs. Neural Netw, 2019, 119: 1-9.
2.	Chabuda A, Dovgialo M, Duszyk A, et al. Successful BCI communication via high-frequency SSVEP or visual, audio or tactile P300 in 30 tested volunteers. Acta Neurobiol Exp, 2019, 79(4): 421-431.
3.	Ge Sheng, Jiang Yichuan, Zhang Mingming, et al. SSVEP-based brain-computer interface with a limited number of frequencies based on dual-frequency biased coding. IEEE T Neur Sys Reh, 2021, 29: 760-769.
4.	Zhao Xi, Wang Zhenyu, Zhang Min, et al. A comfortable steady state visual evoked potential stimulation paradigm using peripheral vision. J Neural Eng, 2021, 18(5): 056021.
5.	Lee M-H, Kwon O-Y, Kim Y-J, et al. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Gigascience, 2019, 8(5): giz002.
6.	Chen Yonghao, Yang Chen, Ye Xiaochen, et al. Implementing a calibration-free SSVEP-based BCI system with 160 targets. J Neural Eng, 2021, 18(4): 046094.
7.	Floriano A, Delisle-R D, Diez P F, et al. Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on depth-of-field. Comput Meth Prog Bio, 2020, 184: 105271.
8.	Floriano A, Carmona V L, Diez P F, et al. A study of SSVEP from below-the-hairline areas in low-, medium-, and high-frequency ranges. Res Biomed Eng, 2019, 35(1): 71-76.
9.	Peng Yufan, Wong C M, Wang Ze, et al. Fatigue detection in SSVEP-BCIs based on wavelet entropy of EEG. IEEE Access, 2021, 9: 114905-114913.
10.	Ehlers J, Lueth T, Graeser A. High frequency steady-state visual evoked potentials: an empirical study on re-test stability for brain-computer interface usage// Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications. CHIRA: Science and Technology Publications, 2019: 164-170.
11.	Chen Xiaogang, Zhao Bing, Wang Yijun, et al. Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm. J Neural Eng, 2019, 16(2): 026012.
12.	Li A, Alimanov K, Fazli S, et al. Towards paradigm-independent brain computer interfaces// Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2020: 1-6.
13.	Xu Lichao, Xu Minpeng, Jung T-P, et al. Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface. Cogn Neurodyn, 2021, 15(4): 569-584.
14.	Abdelnabi S, Huang Xuelin, Bulling A. Towards high-frequency SSVEP-based target discrimination with an extended alphanumeric keyboard// Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari: IEEE, 2019: 4181-4186.
15.	Won D O, Zhang Haihong, Guan Cuntai, et al. A BCI speller based on SSVEP using high frequency stimuli design// Proceedings of the IEEE International Conference on Systems. San Diego: IEEE, 2014: 1068-1071.
16.	Chen Xiaogang, Chen Zhikai, Gao Shangkai, et al. A high-ITR SSVEP-based BCI speller. Brain-Comput Interfa, 2014, 1(3-4): 181-191.
17.	Saboor A, Benda M, Rezeika A, et al. Mesh of SSVEP-based BCI and eye-tracker for use of higher frequency stimuli and lower number of EEG channels// Proceedings of the 16th International Conference on Frontiers of Information Technology (FIT). Islamabad: IEEE, 2018: 99-104.
18.	Chabuda A, Durka P, Zygierewicz J. High frequency SSVEP-BCI with hardware stimuli control and phase-synchronized comb filter. IEEE T Neur Sys Reh, 2018, 26(2): 344-352.
19.	Abiri R, Borhani S, Sellers E W, et al. A comprehensive review of EEG-based brain–computer interface paradigms. J Neural Eng, 2019, 16(1): 011001.
20.	Mao Xiaoqian, Li Wei, Hu Hong, et al. Improve the classification efficiency of high-frequency phase-tagged SSVEP by a recursive Bayesian-based approach. IEEE T Neur Sys Reh, 2020, 28(3): 561-572.
21.	Tsoneva T, Garcia-Molina G, Desain P. SSVEP phase synchronies and propagation during repetitive visual stimulation at high frequencies. Sci Rep, 2021, 11(1): 4975.
22.	Zhu Danhua, Garcia-Molina G, Mihajlović V, et al. Online BCI implementation of high-frequency phase modulated visual stimuli// Proceedings of the International Conference on Universal Access in Human-Computer Interaction. Hangzhou: Springer, 2011: 645-654.
23.	Tong Jijun, Zhu Danhua. Multi-phase cycle coding for SSVEP based brain-computer interfaces. Biomed Eng Online, 2015, 14(1): 1-13.
24.	Jiang Lu, Pei Weihua, Wang Yijun. A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60Hz. China Commun, 2022, 19(2): 1-14.
25.	Hu Hong, Zhao Jing, Li Hongbo, et al. Telepresence control of humanoid robot via high-frequency phase-tagged SSVEP stimuli// Proceedings of the IEEE International Workshop on Advanced Motion Control. Auckland: IEEE, 2016: 214-219.
26.	Keihani A, Shirzhiyan Z, Farahi M, et al. Use of sine shaped high-frequency rhythmic visual stimuli patterns for SSVEP response analysis and fatigue rate evaluation in normal subjects. Front Hum Neurosci, 2018, 12: 201.
27.	Ajami S, Mahnam A, Abootalebi V. Development of a practical high frequency brain-computer interface based on steady-state visual evoked potentials using a single channel of EEG. Biocybern Biomed Eng, 2018, 38(1): 106-114.
28.	Ming Gege, Wang Yijun, Pei Weihua, et al. Optimizing spatial contrast of a new checkerboard stimulus for eliciting robust SSVEPs// Proceedings of the 9th IEEE/EMBS International Conference on Neural Engineering (NER). San Francisco: IEEE, 2019: 175-178.
29.	Ming Gege, Pei Weihua, Chen Hongda, 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.
30.	Liang Liyan, Yang Chen, Wang Yijun, et al. High-frequency SSVEP stimulation paradigm based on dual frequency modulation// Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 6184-6187.
31.	Yue Liang, Xiao Xiaolin, Xu Minpeng, et al. A brain-computer interface based on high-frequency steady-state asymmetric visual evoked potentials// Proceedings of the 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC). Montreal: IEEE, 2020: 3090-3093.
32.	Materka A, Byczyk M, Poryzala P. A virtual keypad based on alternate half-field stimulated visual evoked potentials// 2007 International Symposium on Information Technology Convergence (ISITC 2007). Jeonju: IEEE, 2007: 296-300.
33.	Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B, 1996, 58(1): 267-288.
34.	Lin Zhonglin, Zhang Changshui, Wu Wei, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans BioMed Eng, 2007, 54(6): 1172-1176.
35.	Wong C M, Wang Boyu, Wang Ze, et al. Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements. IEEE Trans BioMed Eng, 2020, 67(11): 3057-3072.
36.	Nakanishi M, Wang Yijun, Chen Xiaogang, 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.
37.	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.
38.	Haque I R I, Neubert J. Deep learning approaches to biomedical image segmentation. Inf Med Unlocked, 2020, 18: 100297.
39.	Monga V, Li Yuelong, Eldar Y C. Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing. IEEE Signal Proc Mag, 2021, 38(2): 18-44.
40.	van den Berg B, van Donkelaar S, Alimardani M. Inner speech classification using EEG signals: a deep learning approach// Proceedings of the 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS). Magdeburg: IEEE, 2021: 1-4.
41.	Armengol-Urpi A, Salazar-Gómez A F, Sarma S E. Brainwave-augmented eye tracker: high-frequency SSVEPs improves camera-based eye tracking accuracy// Proceedings of the 27th International Conference on Intelligent User Interfaces. Helsinki: Association for Computing Machinery, 2022: 258-276.
42.	Qin Ke, Wang Raofen, Zhang Yu. Filter bank-driven multivariate synchronization index for training-free SSVEP BCI. IEEE T Neur Sys Reh, 2021, 29: 934-943.
43.	Hübner R, Foleis J H, Ruiz L B, et al. A brain-computer interface based on SSVEP that uses a high-frequency stimulus for decision-making// Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Melbourne: IEEE, 2021: 709-714.
44.	Yang Chen, Li Xiang, Shi Nanlin, et al. Brain–computer interface research. Berlin: Springer, 2020: 87-105.
45.	Hsu C-C, Yeh C-L, Lee W-K, et al. Extraction of high-frequency SSVEP for BCI control using iterative filtering based empirical mode decomposition. Biomed Signal Proces, 2020, 61: 102022.
46.	Xu Minpeng, He Feng, Jung T-P, et al. Current challenges for the practical application of electroencephalography-based brain–computer interfaces. Engineering, 2021, 7(12): 1710-1712.
47.	da Silva A D, da Cruz Júnior G, Júnior C G P. A fast and accurate SSVEP brain machine interface using dry electrodes and high frequency stimuli by employing ensemble learning. IEEE Lat Am T, 2020, 18(6): 1000-1007.
48.	Chai X, Zhang Z, Guan K, et al. A hybrid BCI-controlled smart home system combining SSVEP and EMG for individuals with paralysis. Biomed Signal Proces, 2020, 56: 101687.
49.	Benda M, Genbler F, Stawicki P, et al. Custom-made monitor for easy high-frequency SSVEP stimulation// Proceedings of the International Work-Conference on Artificial Neural Networks. Gran Canaria: Springer, 2019: 382-393.

1. Zhang Yangsong, Yin Erwei, Li Fali, et al. Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs. Neural Netw, 2019, 119: 1-9.
2. Chabuda A, Dovgialo M, Duszyk A, et al. Successful BCI communication via high-frequency SSVEP or visual, audio or tactile P300 in 30 tested volunteers. Acta Neurobiol Exp, 2019, 79(4): 421-431.
3. Ge Sheng, Jiang Yichuan, Zhang Mingming, et al. SSVEP-based brain-computer interface with a limited number of frequencies based on dual-frequency biased coding. IEEE T Neur Sys Reh, 2021, 29: 760-769.
4. Zhao Xi, Wang Zhenyu, Zhang Min, et al. A comfortable steady state visual evoked potential stimulation paradigm using peripheral vision. J Neural Eng, 2021, 18(5): 056021.
5. Lee M-H, Kwon O-Y, Kim Y-J, et al. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Gigascience, 2019, 8(5): giz002.
6. Chen Yonghao, Yang Chen, Ye Xiaochen, et al. Implementing a calibration-free SSVEP-based BCI system with 160 targets. J Neural Eng, 2021, 18(4): 046094.
7. Floriano A, Delisle-R D, Diez P F, et al. Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on depth-of-field. Comput Meth Prog Bio, 2020, 184: 105271.
8. Floriano A, Carmona V L, Diez P F, et al. A study of SSVEP from below-the-hairline areas in low-, medium-, and high-frequency ranges. Res Biomed Eng, 2019, 35(1): 71-76.
9. Peng Yufan, Wong C M, Wang Ze, et al. Fatigue detection in SSVEP-BCIs based on wavelet entropy of EEG. IEEE Access, 2021, 9: 114905-114913.
10. Ehlers J, Lueth T, Graeser A. High frequency steady-state visual evoked potentials: an empirical study on re-test stability for brain-computer interface usage// Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications. CHIRA: Science and Technology Publications, 2019: 164-170.
11. Chen Xiaogang, Zhao Bing, Wang Yijun, et al. Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm. J Neural Eng, 2019, 16(2): 026012.
12. Li A, Alimanov K, Fazli S, et al. Towards paradigm-independent brain computer interfaces// Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2020: 1-6.
13. Xu Lichao, Xu Minpeng, Jung T-P, et al. Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface. Cogn Neurodyn, 2021, 15(4): 569-584.
14. Abdelnabi S, Huang Xuelin, Bulling A. Towards high-frequency SSVEP-based target discrimination with an extended alphanumeric keyboard// Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari: IEEE, 2019: 4181-4186.
15. Won D O, Zhang Haihong, Guan Cuntai, et al. A BCI speller based on SSVEP using high frequency stimuli design// Proceedings of the IEEE International Conference on Systems. San Diego: IEEE, 2014: 1068-1071.
16. Chen Xiaogang, Chen Zhikai, Gao Shangkai, et al. A high-ITR SSVEP-based BCI speller. Brain-Comput Interfa, 2014, 1(3-4): 181-191.
17. Saboor A, Benda M, Rezeika A, et al. Mesh of SSVEP-based BCI and eye-tracker for use of higher frequency stimuli and lower number of EEG channels// Proceedings of the 16th International Conference on Frontiers of Information Technology (FIT). Islamabad: IEEE, 2018: 99-104.
18. Chabuda A, Durka P, Zygierewicz J. High frequency SSVEP-BCI with hardware stimuli control and phase-synchronized comb filter. IEEE T Neur Sys Reh, 2018, 26(2): 344-352.
19. Abiri R, Borhani S, Sellers E W, et al. A comprehensive review of EEG-based brain–computer interface paradigms. J Neural Eng, 2019, 16(1): 011001.
20. Mao Xiaoqian, Li Wei, Hu Hong, et al. Improve the classification efficiency of high-frequency phase-tagged SSVEP by a recursive Bayesian-based approach. IEEE T Neur Sys Reh, 2020, 28(3): 561-572.
21. Tsoneva T, Garcia-Molina G, Desain P. SSVEP phase synchronies and propagation during repetitive visual stimulation at high frequencies. Sci Rep, 2021, 11(1): 4975.
22. Zhu Danhua, Garcia-Molina G, Mihajlović V, et al. Online BCI implementation of high-frequency phase modulated visual stimuli// Proceedings of the International Conference on Universal Access in Human-Computer Interaction. Hangzhou: Springer, 2011: 645-654.
23. Tong Jijun, Zhu Danhua. Multi-phase cycle coding for SSVEP based brain-computer interfaces. Biomed Eng Online, 2015, 14(1): 1-13.
24. Jiang Lu, Pei Weihua, Wang Yijun. A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60Hz. China Commun, 2022, 19(2): 1-14.
25. Hu Hong, Zhao Jing, Li Hongbo, et al. Telepresence control of humanoid robot via high-frequency phase-tagged SSVEP stimuli// Proceedings of the IEEE International Workshop on Advanced Motion Control. Auckland: IEEE, 2016: 214-219.
26. Keihani A, Shirzhiyan Z, Farahi M, et al. Use of sine shaped high-frequency rhythmic visual stimuli patterns for SSVEP response analysis and fatigue rate evaluation in normal subjects. Front Hum Neurosci, 2018, 12: 201.
27. Ajami S, Mahnam A, Abootalebi V. Development of a practical high frequency brain-computer interface based on steady-state visual evoked potentials using a single channel of EEG. Biocybern Biomed Eng, 2018, 38(1): 106-114.
28. Ming Gege, Wang Yijun, Pei Weihua, et al. Optimizing spatial contrast of a new checkerboard stimulus for eliciting robust SSVEPs// Proceedings of the 9th IEEE/EMBS International Conference on Neural Engineering (NER). San Francisco: IEEE, 2019: 175-178.
29. Ming Gege, Pei Weihua, Chen Hongda, 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.
30. Liang Liyan, Yang Chen, Wang Yijun, et al. High-frequency SSVEP stimulation paradigm based on dual frequency modulation// Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 6184-6187.
31. Yue Liang, Xiao Xiaolin, Xu Minpeng, et al. A brain-computer interface based on high-frequency steady-state asymmetric visual evoked potentials// Proceedings of the 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC). Montreal: IEEE, 2020: 3090-3093.
32. Materka A, Byczyk M, Poryzala P. A virtual keypad based on alternate half-field stimulated visual evoked potentials// 2007 International Symposium on Information Technology Convergence (ISITC 2007). Jeonju: IEEE, 2007: 296-300.
33. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B, 1996, 58(1): 267-288.
34. Lin Zhonglin, Zhang Changshui, Wu Wei, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans BioMed Eng, 2007, 54(6): 1172-1176.
35. Wong C M, Wang Boyu, Wang Ze, et al. Spatial filtering in SSVEP-based BCIs: Unified framework and new improvements. IEEE Trans BioMed Eng, 2020, 67(11): 3057-3072.
36. Nakanishi M, Wang Yijun, Chen Xiaogang, 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.
37. 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.
38. Haque I R I, Neubert J. Deep learning approaches to biomedical image segmentation. Inf Med Unlocked, 2020, 18: 100297.
39. Monga V, Li Yuelong, Eldar Y C. Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing. IEEE Signal Proc Mag, 2021, 38(2): 18-44.
40. van den Berg B, van Donkelaar S, Alimardani M. Inner speech classification using EEG signals: a deep learning approach// Proceedings of the 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS). Magdeburg: IEEE, 2021: 1-4.
41. Armengol-Urpi A, Salazar-Gómez A F, Sarma S E. Brainwave-augmented eye tracker: high-frequency SSVEPs improves camera-based eye tracking accuracy// Proceedings of the 27th International Conference on Intelligent User Interfaces. Helsinki: Association for Computing Machinery, 2022: 258-276.
42. Qin Ke, Wang Raofen, Zhang Yu. Filter bank-driven multivariate synchronization index for training-free SSVEP BCI. IEEE T Neur Sys Reh, 2021, 29: 934-943.
43. Hübner R, Foleis J H, Ruiz L B, et al. A brain-computer interface based on SSVEP that uses a high-frequency stimulus for decision-making// Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Melbourne: IEEE, 2021: 709-714.
44. Yang Chen, Li Xiang, Shi Nanlin, et al. Brain–computer interface research. Berlin: Springer, 2020: 87-105.
45. Hsu C-C, Yeh C-L, Lee W-K, et al. Extraction of high-frequency SSVEP for BCI control using iterative filtering based empirical mode decomposition. Biomed Signal Proces, 2020, 61: 102022.
46. Xu Minpeng, He Feng, Jung T-P, et al. Current challenges for the practical application of electroencephalography-based brain–computer interfaces. Engineering, 2021, 7(12): 1710-1712.
47. da Silva A D, da Cruz Júnior G, Júnior C G P. A fast and accurate SSVEP brain machine interface using dry electrodes and high frequency stimuli by employing ensemble learning. IEEE Lat Am T, 2020, 18(6): 1000-1007.
48. Chai X, Zhang Z, Guan K, et al. A hybrid BCI-controlled smart home system combining SSVEP and EMG for individuals with paralysis. Biomed Signal Proces, 2020, 56: 101687.
49. Benda M, Genbler F, Stawicki P, et al. Custom-made monitor for easy high-frequency SSVEP stimulation// Proceedings of the International Work-Conference on Artificial Neural Networks. Gran Canaria: Springer, 2019: 382-393.

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