Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials_Journal of Biomedical Engineering

Authors：

WANG Jinhai ¹ , WANG Kangning ^1,2 , CHEN Xiaogang ² , WANG Huiquan ¹ , XU Shengpu ² ,  LIU Ming ²

1. School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China;
2. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, P.R.China;

Corresponding author：

LIU Ming, Email: liuming_tju@163.com

Keywords：

brain-controlled wheelchair; simulation training; brain-computer interface; steady-state visual evoked potentials

DOI：

10.7507/1001-5515.201906025

Video：

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Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively (P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% (P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.

Citation： WANG Jinhai, WANG Kangning, CHEN Xiaogang, WANG Huiquan, XU Shengpu, LIU Ming. Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials. Journal of Biomedical Engineering, 2020, 37(3): 502-511. doi: 10.7507/1001-5515.201906025 Copy

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3.	Yu Yang, Liu Yadong, Jiang Jun, et al. An asynchronous control paradigm based on sequential motor imagery and its application in wheelchair navigation. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(12): 2367-2375.
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7.	Müller S T, Celeste W C, Bastos-Filho T F. Brain-computer interface based on visual evoked potentials to command autonomous robotic wheelchair. J Med Biol Eng, 2010, 30(6): 407-416.
8.	Long Jinyi, Li Yuanqing, Wang Hongtao, et al. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(5): 720-729.
9.	Yu Yang, Zhou Zongtan, Liu Yadong, et al. Self-paced operation of a wheelchair based on a hybrid brain-computer interface combining motor imagery and P300 potential. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(12): 2516-2526.
10.	Bi Luzheng, Fan Xinan, Liu Yili. EEG-based brain-controlled mobile robots: a survey. IEEE T Hum-Mach Syst, 2013, 43(2): 161-176.
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12.	Gentiletti G G, Gebhart J G, Acevedo R C, et al. Command of a simulated wheelchair on a virtual environment using a brain-computer interface. IRBM, 2009, 30(5): 218-225.
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14.	Velasco-Álvarez F, Ron-Angevin R, Silva-Sauer L D, et al. Audio-cued motor imagery-based brain-computer interface: navigation through virtual and real environments. Neurocomputing, 2013, 121: 89-98.
15.	Herweg A, Gutzeit J, Kleih S, et al. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol Psychol, 2016, 121(Pt A): 117-124.
16.	Yu Tianyou, Xiao Jun, Wang Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Trans Biomed Eng, 2015, 62(7): 1706-1717.
17.	Baykara E, Ruf C A, Fioravanti C, et al. Effects of training and motivation on auditory P300 brain-computer interface performance. Clin Neurophysiol, 2016, 127(1): 379-387.
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25.	Pastor M A, Artieda J, Arbizu J, et al. Human cerebral activation during steady-state visual-evoked responses. J Neurosci, 2003, 23(37): 11621-11627.
26.	Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008.
27.	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.
28.	陈小刚, 赵秉, 刘明, 等. 稳态视觉诱发电位脑-机接口控制机械臂系统的设计与实现. 生物医学工程与临床, 2018, 22(3): 20-26.
29.	陈若男, 文聪聪, 彭玲, 等. 改进 A*算法在机器人室内路径规划中的应用. 计算机应用, 2019, 39(4): 1006-1011.
30.	Iturrate I, Antelis J M, Kubler A, et al. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans Robot, 2009, 25(3): 614-627.

1. Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 164-173.
2. Tanaka K, Matsunaga K, Wang H O. Electroencephalogram-based control of an electric wheelchair. IEEE Trans Robot, 2005, 21(4): 762-766.
3. Yu Yang, Liu Yadong, Jiang Jun, et al. An asynchronous control paradigm based on sequential motor imagery and its application in wheelchair navigation. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(12): 2367-2375.
4. Rebsamen B, Guan C, Zhang H, et al. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(6): 590-598.
5. Tang Jingsheng, Liu Yadong, Hu Dewen, et al. Towards BCI-actuated smart wheelchair system. Biomed Eng Online, 2018, 17(1): 111.
6. Diez P F, Müller S T, 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.
7. Müller S T, Celeste W C, Bastos-Filho T F. Brain-computer interface based on visual evoked potentials to command autonomous robotic wheelchair. J Med Biol Eng, 2010, 30(6): 407-416.
8. Long Jinyi, Li Yuanqing, Wang Hongtao, et al. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(5): 720-729.
9. Yu Yang, Zhou Zongtan, Liu Yadong, et al. Self-paced operation of a wheelchair based on a hybrid brain-computer interface combining motor imagery and P300 potential. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(12): 2516-2526.
10. Bi Luzheng, Fan Xinan, Liu Yili. EEG-based brain-controlled mobile robots: a survey. IEEE T Hum-Mach Syst, 2013, 43(2): 161-176.
11. Leeb R, Friedman D, Müller-Putz G R, et al. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci, 2007(2): 79642.
12. Gentiletti G G, Gebhart J G, Acevedo R C, et al. Command of a simulated wheelchair on a virtual environment using a brain-computer interface. IRBM, 2009, 30(5): 218-225.
13. Huang Dandan, Qian Kai, Fei Dingyu, et al. Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(3): 379-388.
14. Velasco-Álvarez F, Ron-Angevin R, Silva-Sauer L D, et al. Audio-cued motor imagery-based brain-computer interface: navigation through virtual and real environments. Neurocomputing, 2013, 121: 89-98.
15. Herweg A, Gutzeit J, Kleih S, et al. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol Psychol, 2016, 121(Pt A): 117-124.
16. Yu Tianyou, Xiao Jun, Wang Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Trans Biomed Eng, 2015, 62(7): 1706-1717.
17. Baykara E, Ruf C A, Fioravanti C, et al. Effects of training and motivation on auditory P300 brain-computer interface performance. Clin Neurophysiol, 2016, 127(1): 379-387.
18. Wan Feng, Cruz J D, Nan Wenya, et al. Alpha neurofeedback training improves SSVEP-based BCI performance. J Neural Eng, 2016, 13(3): 036019.
19. Rose F D, Attree E A, Brooks B M, et al. Training in virtual environments: transfer to real world tasks and equivalence to real task training. Ergonomics, 2000, 43(4): 494-511.
20. Kenyon R V, Afenya M B. Training in virtual and real environments. Ann Biomed Eng, 1995, 23(4): 445-455.
21. Holden M K. Virtual environments for motor rehabilitation: review. Cyberpsychol Behav, 2005, 8(3): 187-211.
22. Todorov E, Shadmehr R, Bizzi E. Augmented feedback presented in a virtual environment accelerates learning of a difficult motor task. J Mot Behav, 1997, 29(2): 147-158.
23. 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.
24. Chen Xiaogang, Chen Zhikai, Gao Shangkai, et al. A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces, 2014, 1(3-4): 181-191.
25. Pastor M A, Artieda J, Arbizu J, et al. Human cerebral activation during steady-state visual-evoked responses. J Neurosci, 2003, 23(37): 11621-11627.
26. Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008.
27. 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.
28. 陈小刚, 赵秉, 刘明, 等. 稳态视觉诱发电位脑-机接口控制机械臂系统的设计与实现. 生物医学工程与临床, 2018, 22(3): 20-26.
29. 陈若男, 文聪聪, 彭玲, 等. 改进 A*算法在机器人室内路径规划中的应用. 计算机应用, 2019, 39(4): 1006-1011.
30. Iturrate I, Antelis J M, Kubler A, et al. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans Robot, 2009, 25(3): 614-627.

Journal of Biomedical Engineering

Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials

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