A portable steady-state visual evoked potential brain-computer interface system for smart healthcare_Journal of Biomedical Engineering

Authors：

ZHU Yisen ¹ , JI Zhouyu ¹ , LI Shuran ² , WANG Haicheng ¹ , FU Yunfa ³ ,  WANG Hongtao ¹

1. College of Electronics and Information Engineering, Wuyi University, Jiangmen, Guangdong 529020, P. R. China;
2. School of Electronic & Information Engineering and Communication Engineering, Guangzhou City University of Technology, Guangdong 510800, P. R. China;
3. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;

Corresponding author：

WANG Hongtao, Email: hongtaowang@wyu.edu.cn

Keywords：

Brain-computer interface; Steady-state visual evoked potential; Filter bank canonical correlation analysis; Medical rehabilitation assistance

DOI：

10.7507/1001-5515.202412051

Video：

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This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.

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11.	付佳钰, 王丽平. 基于脑电图的无创脑机接口的临床应用进展. 医学综述, 2021, 27(23): 4619-4623.
12.	Birbaumer N, Ramos Murguialday A, Weber C, et al. Neurofeedback and brain-computer interface clinical applications. Int Rev Neurobiol, 2009, 86: 107-117.
13.	Spataro R, Xu Y, Xu R, et al. How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: a comparative study. Front Neurosci, 2022, 16: 959339.
14.	Sebastián-Romagosa M, Cho W, Ortner R, et al. Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients-a feasibility study. Front Neurosci, 2020, 14: 591435.
15.	Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng, 2020, 17(4): 041001.
16.	Chen J, Liu Q, Tan C, et al. Non-invasive brain-computer interfaces effectively improve motor function, sensory function, and activities of daily living in patients with spinal cord injury: a systematic review and meta-analysis. Brain Network and Modulation, 2024, 3(1): 9-19.
17.	李丹, 刘玲玉, 靳令经, 等. 基于脑机接口的康复训练在脑卒中上肢康复中的研究进展. 中国康复, 2023, 38(10): 621-625.
18.	Xie X, Shi R, Yu H, et al. Executive function rehabilitation and evaluation based on brain-computer interface and virtual reality: our opinion. Front Neurosci, 2024, 18: 1377097.
19.	Chen J, Zhang Y, Pan Y, et al. A transformer-based deep neural network model for SSVEP classification. Neural Networks, 2023, 164: 521-534.
20.	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.
21.	Zhang Y, Xu P, Liu T, et al. Multiple frequencies sequential coding for SSVEP-based brain-computer interface. Plos One, 2012, 7(3): e29519.
22.	Zhu D, Bieger J, Garcia Molina G, et al. A survey of stimulation methods used in SSVEP-based BCIs. Computational Intelligence and Neuroscience, 2010, 2010: 702357.
23.	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.
24.	Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Transactions on Biomedical Engineering, 2006, 53(12): 2610-2614.
25.	Nakanishi M, Wang Y, Wang Y T, et al. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. Plos One, 2015, 10(10): e0140703.
26.	Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. Journal of Neural Engineering, 2015, 12(4): 046008.
27.	Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Transactions on Biomedical Engineering, 2018, 65(1): 104-112.
28.	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.
29.	Wang H, Li T, Huang Z. Remote control of an electrical car with SSVEP-based BCI// 2010 IEEE International Conference on Information Theory and Information Security. Beijing: IEEE, 2010: 837-840.
30.	Peng Y, Wong C M, Wang Z, et al. Fatigue detection in SSVEP-BCIs based on wavelet entropy of EEG. IEEE Access, 2021, 9: 114905-114913.
31.	Yin H, Ji Z, Lian Z, et al. Application of kurtosis based dynamic window to enhance SSVEP recognition//2022 China Automation Congress (CAC). Xiamen: IEEE, 2022: 571-576.
32.	Chen X, Chen Z, Gao S, et al. A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces, 2014, 1(3-4): 181-191.
33.	Yan W, Xu G, Du Y, et al. SSVEP-EEG feature enhancement method using an image sharpening filter. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 115-123.
34.	Ji Z, Xu T, Chen C, et al. Subject-specific CNN model with parameter-based transfer learning for SSVEP detection. Biomedical Signal Processing and Control, 2024, 28(11): 1-9.
35.	Wang H, Wang Z, Sun Y, et al. A cascade xDAWN EEGNet structure for unified visual-evoked related potential detection. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 2270-2280.
36.	Wang Z, Chen C, Li J, et al. ST-CapsNet: linking spatial and temporal attention with capsule network for P300 detection improvement. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 991-1000.
37.	Wang H, Xu T, Tang C, et al. Diverse feature blend based on filter-bank common spatial pattern and brain functional connectivity for multiple motor imagery detection. IEEE Access, 2020, 8: 155590-155601.
38.	Zhang H, Wang Z, Yu Y, et al. An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task. Brain Science Advances, 2022, 8(2): 111-126.
39.	Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng, 2000, 8(2): 164-173.
40.	Walter S, Quigley C, Andersen S K, et al. Effects of overt and covert attention on the steady-state visual evoked potential. Neurosci Lett, 2012, 519(1): 37-41.

1. Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease 2021: Findings from the GBD 2021 Study. (2024) [2024-12-15]. https://www.healthdata.org/research-analysis/library/global-burden-disease-2021-findings-gbd-2021-study.
2. 邵谢宁, 张艺滢, 张栋, 等. 融合多感官刺激的虚拟现实—脑机接口手功能增强康复系统. 生物医学工程学杂志, 2024, 41(4): 656-663.
3. 杨雪艳, 辉沐吟, 李寿蓉, 脑卒中患者早期康复时机及方法的研究进展. 临床护理进展, 2022, 1(3): 49-51.
4. Bauer G, Gerstenbrand F, Rumpl E. Varieties of the locked-in syndrome. J Neurol, 1979, 221(2): 77-91.
5. Cunha-Oliveira T, Montezinho L, Simes R F, et al. Mitochondria: a promising convergent target for the treatment of amyotrophic lateral sclerosis. Cells, 2024, 13(3): 248.
6. 王润, 周春兰, 玉美, 等. 卒中后失语患者言语康复管理的证据总结. 护理学报, 2021, 28(11): 5.
7. Schnetzer L, Mccoy M, Bergmann J, et al. Locked-in syndrome revisited. Therapeutic Advances in Neurological Disorders, 2023, 16: 17562864231160873.
8. Genuis S K, Luth W, Bubela T, et al. What do people affected by amyotrophic lateral sclerosis want from health communications? Evidence from the ALS Talk Project. Muscle Nerve, 2023, 68(3): 286-295.
9. Stransky M L, Jensen K M, Morris M A. Adults with communication disabilities experience poorer health and healthcare outcomes compared to persons without communication disabilities. J Gen Intern Med, 2018, 33(12): 2147-2155.
10. Wolpaw J R, Birbaumer N, Mcfarland D J, et al. Brain-computer interfaces for communication and control. Suppl Clin Neurophysiol, 2002, 113(6): 767-791.
11. 付佳钰, 王丽平. 基于脑电图的无创脑机接口的临床应用进展. 医学综述, 2021, 27(23): 4619-4623.
12. Birbaumer N, Ramos Murguialday A, Weber C, et al. Neurofeedback and brain-computer interface clinical applications. Int Rev Neurobiol, 2009, 86: 107-117.
13. Spataro R, Xu Y, Xu R, et al. How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: a comparative study. Front Neurosci, 2022, 16: 959339.
14. Sebastián-Romagosa M, Cho W, Ortner R, et al. Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients-a feasibility study. Front Neurosci, 2020, 14: 591435.
15. Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng, 2020, 17(4): 041001.
16. Chen J, Liu Q, Tan C, et al. Non-invasive brain-computer interfaces effectively improve motor function, sensory function, and activities of daily living in patients with spinal cord injury: a systematic review and meta-analysis. Brain Network and Modulation, 2024, 3(1): 9-19.
17. 李丹, 刘玲玉, 靳令经, 等. 基于脑机接口的康复训练在脑卒中上肢康复中的研究进展. 中国康复, 2023, 38(10): 621-625.
18. Xie X, Shi R, Yu H, et al. Executive function rehabilitation and evaluation based on brain-computer interface and virtual reality: our opinion. Front Neurosci, 2024, 18: 1377097.
19. Chen J, Zhang Y, Pan Y, et al. A transformer-based deep neural network model for SSVEP classification. Neural Networks, 2023, 164: 521-534.
20. 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.
21. Zhang Y, Xu P, Liu T, et al. Multiple frequencies sequential coding for SSVEP-based brain-computer interface. Plos One, 2012, 7(3): e29519.
22. Zhu D, Bieger J, Garcia Molina G, et al. A survey of stimulation methods used in SSVEP-based BCIs. Computational Intelligence and Neuroscience, 2010, 2010: 702357.
23. 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.
24. Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Transactions on Biomedical Engineering, 2006, 53(12): 2610-2614.
25. Nakanishi M, Wang Y, Wang Y T, et al. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. Plos One, 2015, 10(10): e0140703.
26. Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. Journal of Neural Engineering, 2015, 12(4): 046008.
27. Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Transactions on Biomedical Engineering, 2018, 65(1): 104-112.
28. 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.
29. Wang H, Li T, Huang Z. Remote control of an electrical car with SSVEP-based BCI// 2010 IEEE International Conference on Information Theory and Information Security. Beijing: IEEE, 2010: 837-840.
30. Peng Y, Wong C M, Wang Z, et al. Fatigue detection in SSVEP-BCIs based on wavelet entropy of EEG. IEEE Access, 2021, 9: 114905-114913.
31. Yin H, Ji Z, Lian Z, et al. Application of kurtosis based dynamic window to enhance SSVEP recognition//2022 China Automation Congress (CAC). Xiamen: IEEE, 2022: 571-576.
32. Chen X, Chen Z, Gao S, et al. A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces, 2014, 1(3-4): 181-191.
33. Yan W, Xu G, Du Y, et al. SSVEP-EEG feature enhancement method using an image sharpening filter. IEEE Trans Neural Syst Rehabil Eng, 2022, 30: 115-123.
34. Ji Z, Xu T, Chen C, et al. Subject-specific CNN model with parameter-based transfer learning for SSVEP detection. Biomedical Signal Processing and Control, 2024, 28(11): 1-9.
35. Wang H, Wang Z, Sun Y, et al. A cascade xDAWN EEGNet structure for unified visual-evoked related potential detection. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 2270-2280.
36. Wang Z, Chen C, Li J, et al. ST-CapsNet: linking spatial and temporal attention with capsule network for P300 detection improvement. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 991-1000.
37. Wang H, Xu T, Tang C, et al. Diverse feature blend based on filter-bank common spatial pattern and brain functional connectivity for multiple motor imagery detection. IEEE Access, 2020, 8: 155590-155601.
38. Zhang H, Wang Z, Yu Y, et al. An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task. Brain Science Advances, 2022, 8(2): 111-126.
39. Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng, 2000, 8(2): 164-173.
40. Walter S, Quigley C, Andersen S K, et al. Effects of overt and covert attention on the steady-state visual evoked potential. Neurosci Lett, 2012, 519(1): 37-41.

Journal of Biomedical Engineering

Latest ArticlesA portable steady-state visual evoked potential brain-computer interface system for smart healthcare

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