Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario_Journal of Biomedical Engineering

Authors：

LI Xiaodong ^1,2,3 , CAO Xiang ³ , WANG Junlin ^2,3 , ZHU Weijie ⁴ , HUANG Yong ^5,6 , WAN Feng ⁷ ,  HU Yong ^2,3

1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China;
2. Orthopedic Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong 518053, P. R. China;
3. Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong S. A. R. 999077, P. R. China;
4. Joint Lab of Brain-Verse Digital Convergence, Guangdong Institute of Intelligence Science and Technology, Zhuhai, Guangdong 519060, P. R. China;
5. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P. R. China;
6. Lab of Brain-Inspired Computing System, Guangdong Institute of intelligence Science and Technology, Zhuhai, Guangdong 519060, P. R. China;
7. Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau S. A. R. 999078, P. R. China;

Corresponding author：

HU Yong, Email: yhud@hku.hk

Keywords：

Brain-computer interface; Steady-state visual evoked potential; Wearable system; Simplified experimental preparation

DOI：

10.7507/1001-5515.202310069

Video：

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Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.

1.	Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng, 2020, 17(4): 041001.
2.	Wolpaw J R, Bedlack R S, Reda D J, et al. Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis. Neurology, 2018, 91(3): e258-e267.
3.	Shi N, Wang L, Chen Y, et al. Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report. J Neurorestoratology, 2020, 8(1): 40-52.
4.	Guo N, Wang X, Duanmu D, et al. SSVEP-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1737-1744.
5.	Combaz A, Chatelle C, Robben A, et al. A comparison of two spelling brain-computer interfaces based on visual P3 and SSVEP in locked-in syndrome. PLoS One, 2013, 8(9): e73691.
6.	Chi Y M, Wang Y-T, Wang Y, et al. Dry and noncontact EEG sensors for mobile brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2011, 20(2): 228-235.
7.	吴正平, 魏欢, 赵靖, 等. 基于 SSVEP 的无线脑-机接口系统研究与实现. 中国生物医学工程学报, 2019, 38(1): 120-124.
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9.	Zhu F, Jiang L, Dong G, et al. An open dataset for wearable ssvep-based brain-computer interfaces. Sensors, 2021, 21(4): 1256.
10.	Huggins J E, Wren P A, Gruis K L. What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis. Amyotroph Lateral Scler, 2011, 12(5): 318-324.
11.	Huggins J E, Moinuddin A A, Chiodo A E, et al. What would brain-computer interface users want: opinions and priorities of potential users with spinal cord injury. Arch Phys Med Rehabil, 2015, 96(3): S38-S45. e35.
12.	Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A, 2015, 112(44): E6058-E6067.
13.	Fiedler P, Mühle R, Griebel S, et al. Contact pressure and flexibility of multipin dry EEG electrodes. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(4): 750-757.
14.	Gargiulo G, Calvo R A, Bifulco P, et al. A new EEG recording system for passive dry electrodes. Clin Neurophysiol, 2010, 121(5): 686-693.
15.	Wong C M, Wang Z, Nakanishi M, et al. Online adaptation boosts SSVEP-based BCI performance. IEEE Trans Biomed Eng, 2021, 69(6): 2018-2028.
16.	Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng, 2006, 53(12): 2610-2614.
17.	Lao K F, Wong C M, Wang Z, et al. Learning prototype spatial filters for subject-independent SSVEP-based brain-computer interface// 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Miyazaki: IEEE, 2018: 485-490.
18.	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.
19.	Liu B, Huang X, Wang Y, et al. BETA: A large benchmark database toward SSVEP-BCI application. Front Neurosci, 2020, 14: 627.
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.	Wang Y, Chen X, Gao X, et al. A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2016, 25(10): 1746-1752.
22.	Mu J, Liu S, Burkitt A N, et al. Multi-frequency steady-state visual evoked potential dataset. Sci Data, 2024, 11(1): 26.
23.	Hsu H-T, Shyu K-K, Hsu C-C, et al. Phase-approaching stimulation sequence for SSVEP-based BCI: a practical use in VR/AR HMD. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 2754-2764.
24.	Ge S, Jiang Y, Zhang M, et al. SSVEP-based brain-computer interface with a limited number of frequencies based on dual-frequency biased coding. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 760-769.
25.	Acampora G, Trinchese P, Vitiello A. A dataset of EEG signals from a single-channel SSVEP-based brain computer interface. Data Brief, 2021, 35: 106826.
26.	Dong Y, Tian S. A large database towards user-friendly SSVEP-based BCI. Brain Sci Adv, 2023, 9(4): 297-309.
27.	Zerafa R, Camilleri T, Falzon O, et al. A comparison of a broad range of EEG acquisition devices–is there any difference for SSVEP BCIs?. Brain-Comput Interfaces, 2018, 5(4): 121-131.
28.	Li X, Wang J, Cao X, et al. Evaluation of an online SSVEP-BCI with fast system setup. J Neurorestoratology, 2024, 12(2): 100122.
29.	Kappenman E S, Luck S. The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology, 2010, 47(5): 888-904.
30.	Wang Y, Gao X, Hong B, et al. Brain-computer interfaces based on visual evoked potentials. IEEE Eng Med Biol Mag, 2008, 27(5): 64-71.
31.	Marx E, Benda M, Volosyak I. Optimal electrode positions for an SSVEP-based BCI//2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari, Italy: IEEE, 2019: 2731-2736.
32.	Diez P F, Mut V, Laciar E, et al. A comparison of monopolar and bipolar EEG recordings for SSVEP detection//2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Buenos Aires, Argentina: IEEE, 2010: 5803-5806.
33.	Tang Z, Wang Y, Dong G, et al. Learning to control an SSVEP-based BCI speller in naïve subjects//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju, South Korea: IEEE, 2017: 1934-1937.

1. Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng, 2020, 17(4): 041001.
2. Wolpaw J R, Bedlack R S, Reda D J, et al. Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis. Neurology, 2018, 91(3): e258-e267.
3. Shi N, Wang L, Chen Y, et al. Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report. J Neurorestoratology, 2020, 8(1): 40-52.
4. Guo N, Wang X, Duanmu D, et al. SSVEP-based brain computer interface controlled soft robotic glove for post-stroke hand function rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 1737-1744.
5. Combaz A, Chatelle C, Robben A, et al. A comparison of two spelling brain-computer interfaces based on visual P3 and SSVEP in locked-in syndrome. PLoS One, 2013, 8(9): e73691.
6. Chi Y M, Wang Y-T, Wang Y, et al. Dry and noncontact EEG sensors for mobile brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2011, 20(2): 228-235.
7. 吴正平, 魏欢, 赵靖, 等. 基于 SSVEP 的无线脑-机接口系统研究与实现. 中国生物医学工程学报, 2019, 38(1): 120-124.
8. Soleymanpour R, Patel C, Kim I. Non-contact wearable EEG sensors for SSVEP-based brain computer interface applications// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu: IEEE, 2018: 2016-2019.
9. Zhu F, Jiang L, Dong G, et al. An open dataset for wearable ssvep-based brain-computer interfaces. Sensors, 2021, 21(4): 1256.
10. Huggins J E, Wren P A, Gruis K L. What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis. Amyotroph Lateral Scler, 2011, 12(5): 318-324.
11. Huggins J E, Moinuddin A A, Chiodo A E, et al. What would brain-computer interface users want: opinions and priorities of potential users with spinal cord injury. Arch Phys Med Rehabil, 2015, 96(3): S38-S45. e35.
12. Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A, 2015, 112(44): E6058-E6067.
13. Fiedler P, Mühle R, Griebel S, et al. Contact pressure and flexibility of multipin dry EEG electrodes. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(4): 750-757.
14. Gargiulo G, Calvo R A, Bifulco P, et al. A new EEG recording system for passive dry electrodes. Clin Neurophysiol, 2010, 121(5): 686-693.
15. Wong C M, Wang Z, Nakanishi M, et al. Online adaptation boosts SSVEP-based BCI performance. IEEE Trans Biomed Eng, 2021, 69(6): 2018-2028.
16. Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng, 2006, 53(12): 2610-2614.
17. Lao K F, Wong C M, Wang Z, et al. Learning prototype spatial filters for subject-independent SSVEP-based brain-computer interface// 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Miyazaki: IEEE, 2018: 485-490.
18. 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.
19. Liu B, Huang X, Wang Y, et al. BETA: A large benchmark database toward SSVEP-BCI application. Front Neurosci, 2020, 14: 627.
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. Wang Y, Chen X, Gao X, et al. A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2016, 25(10): 1746-1752.
22. Mu J, Liu S, Burkitt A N, et al. Multi-frequency steady-state visual evoked potential dataset. Sci Data, 2024, 11(1): 26.
23. Hsu H-T, Shyu K-K, Hsu C-C, et al. Phase-approaching stimulation sequence for SSVEP-based BCI: a practical use in VR/AR HMD. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 2754-2764.
24. Ge S, Jiang Y, Zhang M, et al. SSVEP-based brain-computer interface with a limited number of frequencies based on dual-frequency biased coding. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 760-769.
25. Acampora G, Trinchese P, Vitiello A. A dataset of EEG signals from a single-channel SSVEP-based brain computer interface. Data Brief, 2021, 35: 106826.
26. Dong Y, Tian S. A large database towards user-friendly SSVEP-based BCI. Brain Sci Adv, 2023, 9(4): 297-309.
27. Zerafa R, Camilleri T, Falzon O, et al. A comparison of a broad range of EEG acquisition devices–is there any difference for SSVEP BCIs?. Brain-Comput Interfaces, 2018, 5(4): 121-131.
28. Li X, Wang J, Cao X, et al. Evaluation of an online SSVEP-BCI with fast system setup. J Neurorestoratology, 2024, 12(2): 100122.
29. Kappenman E S, Luck S. The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology, 2010, 47(5): 888-904.
30. Wang Y, Gao X, Hong B, et al. Brain-computer interfaces based on visual evoked potentials. IEEE Eng Med Biol Mag, 2008, 27(5): 64-71.
31. Marx E, Benda M, Volosyak I. Optimal electrode positions for an SSVEP-based BCI//2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari, Italy: IEEE, 2019: 2731-2736.
32. Diez P F, Mut V, Laciar E, et al. A comparison of monopolar and bipolar EEG recordings for SSVEP detection//2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Buenos Aires, Argentina: IEEE, 2010: 5803-5806.
33. Tang Z, Wang Y, Dong G, et al. Learning to control an SSVEP-based BCI speller in naïve subjects//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju, South Korea: IEEE, 2017: 1934-1937.

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