Research advances in non-invasive brain-computer interface control strategies_Journal of Biomedical Engineering

Authors：

CAO Hongtao ¹ , JUNG Tzyy-Ping ^1,2,3 , CHEN Yuanfang ⁴ , MEI Jie ² , LI Ang ² ,  XU Minpeng ^1,2 , MING dong ^1,2

1. Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China;
2. School of Precision Instruments and Optoelectronic Engineering, Tianjin University Tianjin 300072, P. R. China;
3. University of California, Swartz Center for Computational Sciences, California 92093, USA;
4. Beijing Institute of Machinery and Equipment, Beijing 100854, P. R. China;

Corresponding author：

Keywords：

Brain-computer interface; Non-invasive; Human-computer interaction; Control strategies

DOI：

10.7507/1001-5515.202205013

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.

Citation： CAO Hongtao, JUNG Tzyy-Ping, CHEN Yuanfang, MEI Jie, LI Ang, XU Minpeng, MING dong. Research advances in non-invasive brain-computer interface control strategies. Journal of Biomedical Engineering, 2022, 39(5): 1033-1040. doi: 10.7507/1001-5515.202205013 Copy

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2.	Branco M P, Pels E G M, Nijboer F, et al. Brain-Computer interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers. Disabil Rehabil Assist Technol, 2021: 1-11.
3.	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.
4.	Han C, Xu G, Xie J, et al. Highly interactive brain computer interface based on flicker-free steady-state motion visual evoked potential. Sci Rep, 2018, 8(1): 1-13.
5.	Wang M, Li R, Zhang R, et al. A wearable SSVEP-based BCI system for quadcopter control using head-mounted device. IEEE Access, 2018, 6: 26789-26798.
6.	Zhang X, Hui L, Wei L, et al. A bibliometric analysis of human-machine interaction methodology for electric-powered wheelchairs driving from 1998 to 2020. Int J Public Health, 2021, 18(14): 7567.
7.	戴廷飞, 刘邈, 叶阳阳, 等. 人机共享控制机器人系统的应用与发展. 仪器仪表学报, 2019, 40(3): 62-73.
8.	Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
9.	Chamola V, Vineet A, Nayyar A, et al. Brain-computer interface-based humanoid control: A review. Sensors, 2020, 20(13): 3620.
10.	Oralhan Z. A new paradigm for region-based P300 speller in brain computer interface. IEEE Access, 2019, 7: 106618-106627.
11.	Ratib S. A smart brain controlled wheelchair based microcontroller system. Int J Artif Intell Appl, 2019, 10(5): 67-85.
12.	Tang J, Liu Y, Hu D, et al. Towards BCI-actuated smart wheelchair system. Biomed Eng Online, 2018, 17(1): 1-22.
13.	Jeong J H, Shim K H, Kim D J, et al. Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(5): 1226-1238.
14.	Aggarwal S, Chugh N. Signal processing techniques for motor imagery brain computer interface: A review. Array, 2019, 1: 100003.
15.	Papanastasiou G P, Drigas A, Lytras M, et al. Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review. Heliyon, 2020, 6(6): e04250.
16.	Wang X, Lu H, Shen X, et al. Prosthetic control system based on motor imagery. Comput Methods Biomech Biomed Engin, 2022, 25(7): 764-771.
17.	Duan X, Xie S, Xie X, et al. Quadcopter flight control using a non-invasive multi-modal brain computer interface. Front Neurorobot, 2019, 13: 23.
18.	Xu Minpeng, Han Jin, Wang Yijun, et al. Implementing over 100 command codes for a high-speed hybrid Brain-Computer Interface using concurrent P300 and SSVEP features. IEEE Trans Biomed Eng, 2020, 67(11): 3073-3082.
19.	Deng X, Yu Z L, Lin C, et al. Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation. J Neural Eng, 2020, 17(4): 045005.
20.	Zheng W, Liu Q, Cen K, et al. Brain-robot shared control based on motor imagery and improved Bayes filter// 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Hong Kong: IEEE, 2019: 139-144.
21.	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.
22.	Wang K, Xu M, Wang Y, et al. Enhance decoding of pre-movement EEG patterns for brain–computer interfaces. J Neural Eng, 2020, 17(1): 016033.
23.	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.
24.	Jin J, Fang H, Daly I, et al. Optimization of model training based on iterative minimum covariance determinant in motor-imagery BCI. Int J Neural Syst, 2021, 31(07): 2150030.
25.	Jeong J H, Kwak N S, Guan C, et al. Decoding movement-related cortical potentials based on subject-dependent and section-wise spectral filtering. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(3): 687-698.
26.	Mishuhina V, Jiang X. Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI. IEEE Signal Process Lett, 2018, 25(6): 783-787.
27.	Deng Y, Li Z, Wang H, et al. Local temporal joint recurrence common spatial patterns for MI-based BCI// 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chongqing: IEEE, 2020, 1: 813-816.
28.	Yu Z, Ma T, Fang N, et al. Local temporal common spatial patterns modulated with phase locking value. Biomed Signal Process Control, 2020, 59: 101882.
29.	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, 2017, 65(1): 104-112.
30.	Zhang Z, Huang Y, Chen S, et al. An intention-driven semi-autonomous intelligent robotic system for drinking. Front Neurorobot, 2017, 11: 48.
31.	Abdulkarim H, Al-Faiz M Z. Brain-controlled wheeled chair path planning for indoor environments. Int J Simul Syst Sci Technol, 2021, 22(1): 10.2.
32.	Xu Y, Ding C, Shu X, et al. Shared control of a robotic arm using non-invasive brain–computer interface and computer vision guidance. Rob Auton Syst, 2019, 115: 121-129.
33.	Tang J, Zhou Z. A shared-control based BCI system: for a robotic arm control// 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS). Harbin: IEEE, 2017: 1-5.
34.	Zhang W, Sun F, Wu H, et al. Asynchronous brain-computer interface shared control of robotic grasping. Tsinghua Sci Technol, 2019, 24(3): 360-370.
35.	Yang C, Wu H, Li Z, et al. Mind control of a robotic arm with visual fusion technology. IEEE Trans Industr Inform, 2017, 14(9): 3822-3830.
36.	Schiatti L, Tessadori J, Deshpande N, et al. Human in the loop of robot learning: EEG-based rewardsignal for target identification and reaching task// 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane: IEEE, 2018: 4473-4480.
37.	张腾, 张小栋, 张英杰, 等. 引入深度强化学习思想的脑-机协作精密操控方法. 西安交通大学学报, 2021, 55(2): 1-9.
38.	Batzianoulis I, Iwane F, Wei S, et al. Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials. Commun Biol, 2021, 4(1): 1-14.
39.	Kar R, Ghosh L, Konar A, et al. EEG-induced autonomous game-teaching to a robot arm by human trainers using reinforcement learning. IEEE Trans Games, 2021: 1. DOI: 10.1109/TG.2021.3124340.
40.	Zhang D W, Meng S S, Deng J C. Research on formation control and cooperative collision avoidance of multi-mobile micro-miniature robots. J Instrum, 2017, 38(3): 578-585.
41.	Petit D, Gergondet P, Cherubini A, et al. An integrated framework for humanoid embodiment with a BCI// 2015 IEEE International Conference on Robotics and Automation (ICRA). Washington: IEEE, 2015: 2882-2887.
42.	Cao L, Li G, Xu Y, et al. A brain-actuated robotic arm system using non-invasive hybrid brain–computer interface and shared control strategy. J Neural Eng, 2021, 18(4): 046045.
43.	Zhang R, Li Y, Yan Y, et al. Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE Trans Neural Syst Rehabil Eng, 2015, 24(1): 128-139.
44.	Schröer S, Killmann I, Frank B, et al. An autonomous robotic assistant for drinking// 2015 IEEE International Conference on Robotics and AUTomation (ICRA). Washington: IEEE, 2015: 6482-6487.
45.	Dai W, Liu Y, Lu H, et al. A shared control framework for human-multirobot foraging with brain-computer interface. IEEE Robot Autom Lett, 2021, 6(4): 6305-6312.
46.	Haddix C, Al-Bakri A F, Sunderam S. Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm. J Neural Eng, 2021, 18(5): 056033.
47.	Han J, Xu M, Wang Y, et al. ‘Write’ but not ‘spell’ Chinese characters with a BCI-controlled robot// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 4741-4744.
48.	Lopes A C, Nunes U, Vaz L, et al. Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces// 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBC). Buenos Aires: IEEE, 2010: 471-474.
49.	Na R, Zheng D, Sun Y, et al. A wearable low-power collaborative sensing system for high-quality SSVEP-BCI signal acquisition. IEEE Internet Things J, 2021, 9(10): 7273-7285.

1. 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.
2. Branco M P, Pels E G M, Nijboer F, et al. Brain-Computer interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers. Disabil Rehabil Assist Technol, 2021: 1-11.
3. 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.
4. Han C, Xu G, Xie J, et al. Highly interactive brain computer interface based on flicker-free steady-state motion visual evoked potential. Sci Rep, 2018, 8(1): 1-13.
5. Wang M, Li R, Zhang R, et al. A wearable SSVEP-based BCI system for quadcopter control using head-mounted device. IEEE Access, 2018, 6: 26789-26798.
6. Zhang X, Hui L, Wei L, et al. A bibliometric analysis of human-machine interaction methodology for electric-powered wheelchairs driving from 1998 to 2020. Int J Public Health, 2021, 18(14): 7567.
7. 戴廷飞, 刘邈, 叶阳阳, 等. 人机共享控制机器人系统的应用与发展. 仪器仪表学报, 2019, 40(3): 62-73.
8. Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
9. Chamola V, Vineet A, Nayyar A, et al. Brain-computer interface-based humanoid control: A review. Sensors, 2020, 20(13): 3620.
10. Oralhan Z. A new paradigm for region-based P300 speller in brain computer interface. IEEE Access, 2019, 7: 106618-106627.
11. Ratib S. A smart brain controlled wheelchair based microcontroller system. Int J Artif Intell Appl, 2019, 10(5): 67-85.
12. Tang J, Liu Y, Hu D, et al. Towards BCI-actuated smart wheelchair system. Biomed Eng Online, 2018, 17(1): 1-22.
13. Jeong J H, Shim K H, Kim D J, et al. Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(5): 1226-1238.
14. Aggarwal S, Chugh N. Signal processing techniques for motor imagery brain computer interface: A review. Array, 2019, 1: 100003.
15. Papanastasiou G P, Drigas A, Lytras M, et al. Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review. Heliyon, 2020, 6(6): e04250.
16. Wang X, Lu H, Shen X, et al. Prosthetic control system based on motor imagery. Comput Methods Biomech Biomed Engin, 2022, 25(7): 764-771.
17. Duan X, Xie S, Xie X, et al. Quadcopter flight control using a non-invasive multi-modal brain computer interface. Front Neurorobot, 2019, 13: 23.
18. Xu Minpeng, Han Jin, Wang Yijun, et al. Implementing over 100 command codes for a high-speed hybrid Brain-Computer Interface using concurrent P300 and SSVEP features. IEEE Trans Biomed Eng, 2020, 67(11): 3073-3082.
19. Deng X, Yu Z L, Lin C, et al. Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation. J Neural Eng, 2020, 17(4): 045005.
20. Zheng W, Liu Q, Cen K, et al. Brain-robot shared control based on motor imagery and improved Bayes filter// 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Hong Kong: IEEE, 2019: 139-144.
21. 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.
22. Wang K, Xu M, Wang Y, et al. Enhance decoding of pre-movement EEG patterns for brain–computer interfaces. J Neural Eng, 2020, 17(1): 016033.
23. 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.
24. Jin J, Fang H, Daly I, et al. Optimization of model training based on iterative minimum covariance determinant in motor-imagery BCI. Int J Neural Syst, 2021, 31(07): 2150030.
25. Jeong J H, Kwak N S, Guan C, et al. Decoding movement-related cortical potentials based on subject-dependent and section-wise spectral filtering. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(3): 687-698.
26. Mishuhina V, Jiang X. Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI. IEEE Signal Process Lett, 2018, 25(6): 783-787.
27. Deng Y, Li Z, Wang H, et al. Local temporal joint recurrence common spatial patterns for MI-based BCI// 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chongqing: IEEE, 2020, 1: 813-816.
28. Yu Z, Ma T, Fang N, et al. Local temporal common spatial patterns modulated with phase locking value. Biomed Signal Process Control, 2020, 59: 101882.
29. 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, 2017, 65(1): 104-112.
30. Zhang Z, Huang Y, Chen S, et al. An intention-driven semi-autonomous intelligent robotic system for drinking. Front Neurorobot, 2017, 11: 48.
31. Abdulkarim H, Al-Faiz M Z. Brain-controlled wheeled chair path planning for indoor environments. Int J Simul Syst Sci Technol, 2021, 22(1): 10.2.
32. Xu Y, Ding C, Shu X, et al. Shared control of a robotic arm using non-invasive brain–computer interface and computer vision guidance. Rob Auton Syst, 2019, 115: 121-129.
33. Tang J, Zhou Z. A shared-control based BCI system: for a robotic arm control// 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS). Harbin: IEEE, 2017: 1-5.
34. Zhang W, Sun F, Wu H, et al. Asynchronous brain-computer interface shared control of robotic grasping. Tsinghua Sci Technol, 2019, 24(3): 360-370.
35. Yang C, Wu H, Li Z, et al. Mind control of a robotic arm with visual fusion technology. IEEE Trans Industr Inform, 2017, 14(9): 3822-3830.
36. Schiatti L, Tessadori J, Deshpande N, et al. Human in the loop of robot learning: EEG-based rewardsignal for target identification and reaching task// 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane: IEEE, 2018: 4473-4480.
37. 张腾, 张小栋, 张英杰, 等. 引入深度强化学习思想的脑-机协作精密操控方法. 西安交通大学学报, 2021, 55(2): 1-9.
38. Batzianoulis I, Iwane F, Wei S, et al. Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials. Commun Biol, 2021, 4(1): 1-14.
39. Kar R, Ghosh L, Konar A, et al. EEG-induced autonomous game-teaching to a robot arm by human trainers using reinforcement learning. IEEE Trans Games, 2021: 1. DOI: 10.1109/TG.2021.3124340.
40. Zhang D W, Meng S S, Deng J C. Research on formation control and cooperative collision avoidance of multi-mobile micro-miniature robots. J Instrum, 2017, 38(3): 578-585.
41. Petit D, Gergondet P, Cherubini A, et al. An integrated framework for humanoid embodiment with a BCI// 2015 IEEE International Conference on Robotics and Automation (ICRA). Washington: IEEE, 2015: 2882-2887.
42. Cao L, Li G, Xu Y, et al. A brain-actuated robotic arm system using non-invasive hybrid brain–computer interface and shared control strategy. J Neural Eng, 2021, 18(4): 046045.
43. Zhang R, Li Y, Yan Y, et al. Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE Trans Neural Syst Rehabil Eng, 2015, 24(1): 128-139.
44. Schröer S, Killmann I, Frank B, et al. An autonomous robotic assistant for drinking// 2015 IEEE International Conference on Robotics and AUTomation (ICRA). Washington: IEEE, 2015: 6482-6487.
45. Dai W, Liu Y, Lu H, et al. A shared control framework for human-multirobot foraging with brain-computer interface. IEEE Robot Autom Lett, 2021, 6(4): 6305-6312.
46. Haddix C, Al-Bakri A F, Sunderam S. Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm. J Neural Eng, 2021, 18(5): 056033.
47. Han J, Xu M, Wang Y, et al. ‘Write’ but not ‘spell’ Chinese characters with a BCI-controlled robot// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 4741-4744.
48. Lopes A C, Nunes U, Vaz L, et al. Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces// 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBC). Buenos Aires: IEEE, 2010: 471-474.
49. Na R, Zheng D, Sun Y, et al. A wearable low-power collaborative sensing system for high-quality SSVEP-BCI signal acquisition. IEEE Internet Things J, 2021, 9(10): 7273-7285.

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Research advances in non-invasive brain-computer interface control strategies

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