Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology_Journal of Biomedical Engineering

Authors：

LU Xiaotong ^1,2 , DING Peng ^1,2 , LI Siyu ^1,2 , GONG Anmin ³ , ZHAO Lei ^2,4 , QIAN Qian ^1,2,6 , SU Lei ^1,2 ,  FU Yunfa ^1,2,5,6

1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China;
2. Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China;
3. College of Information Engineering, Engineering University of PAP, Xi’an 710000, P.R.China;
4. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P.R.China;
5. School of Medicine, Kunming University of Science and Technology, Kunming 650500, P.R.China;
6. Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming 650500, P.R.China;

Corresponding author：

FU Yunfa, Email: fyf@ynu.edu.cn

Keywords：

brain-computer interface; human factors engineering; human factors engineering of brain-computer interface; human-centred brain-computer interface design; satisfaction of brain-computer interface

DOI：

10.7507/1001-5515.202101093

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Abstract Full text Figures/Tables Video References Cited by

Brain-computer interface (BCI) is a revolutionizing human-computer Interaction, which is developing towards the direction of intelligent brain-computer interaction and brain-computer intelligent integration. However, the practical application of BCI is facing great challenges. The maturity of BCI technology has not yet reached the needs of users. The traditional design method of BCI needs to be improved. It is necessary to pay attention to BCI human factors engineering, which plays an important role in narrowing the gap between research and practical application, but it has not attracted enough attention and has not been specifically discussed in depth. Aiming at BCI human factors engineering, this article expounds the design requirements (from users), design ideas, objectives and methods, as well as evaluation indexes of BCI with the human-centred-design. BCI human factors engineering is expected to make BCI system design under different use conditions more in line with human characteristics, abilities and needs, improve the user satisfaction of BCI system, enhance the user experience of BCI system, improve the intelligence of BCI, and make BCI move towards practical application.

Citation： LU Xiaotong, DING Peng, LI Siyu, GONG Anmin, ZHAO Lei, QIAN Qian, SU Lei, FU Yunfa. Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology. Journal of Biomedical Engineering, 2021, 38(2): 210-223. doi: 10.7507/1001-5515.202101093 Copy

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2.	Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin: Springer Publishing Company, 2013.
3.	张旭, 袁芳, 姚兆林. 脑机接口: 电路与系统. 北京: 机械工业出版社, 2020.
4.	Zjajo A. Brain-machine interface: Circuits and systems. Berlin: Springer Publishing Company, 2016.
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6.	Allison B Z, Dunne S, Leeb R, et al. Towards practical Brain-Computer Interfaces: Bridging the gap from research to real-world applications. Berlin: Springer Publishing Company, 2012.
7.	Chavarriaga R, Fried-Oken M, Kleih S, et al. Heading for new shores! Overcoming pitfalls in BCI design. Brain Computer Interfaces, 2017, 4(1-2): 60-73.
8.	Zickler C, Riccio A, Leotta F, et al. A brain-computer interface as input channel for a standard assistive technology software. Clin EEG Neurosci, 2011, 42(4): 236-244.
9.	Riccio A, Pichiorri F, Schettini F, et al. Interfacing brain with computer to improve communication and rehabilitation after brain damage. Prog Brain Res, 2016, 228: 357-387.
10.	Kübler A, Holz E M, Angela R, et al. The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS One, 2014, 9(12): e112392.
11.	许为, 葛列众. 人因学发展的新取向. 心理科学进展, 2018, 26(9): 1521-1534.
12.	蒋祖华. 人因工程. 北京: 科学出版社, 2011.
13.	Giulia L, Alessia P, Luca S, et al. Developing brain-computer interfaces from a user-centered perspective: Assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals. Appl Ergon, 2015, 50: 139-146.
14.	Kübler A, Nijboer F, Kleih S. Hearing the needs of clinical users. Handb Clin Neurol, 2020, 168: 353-368.
15.	Kübler A, Zickler C, Holz E, et al. Applying the user-centred design to evaluation of brain-computer interface controlled applications. Biomed Eng, 2013, 58(15): 3234-3234.
16.	Abiri R, Borhani S, Kilmarx J, et al. A usability study of low-cost wireless brain-computer interface for cursor control using online linear model. IEEE Trans Hum Mach Syst, 2020, 50(4): 287-297.
17.	Kübler A. The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome. Neuroethics, 2020, 13(3): 1-18.
18.	Martin S, Armstrong E, Thomson E, et al. A qualitative study adopting a user-centered approach to design and validate a brain computer interface for cognitive rehabilitation for people with brain injury. Assist Technol, 2018, 30(5): 233-241.
19.	Branco M P, Pels E G M, Sars R H, et al. Brain-computer interfaces for communication: Preferences of individuals with locked-in syndrome. Neurorehab Neural Re, 2021, 35(3): 267-279.
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25.	Kimura M, Nakatani S, Nishida S I, et al. 3D printable dry EEG electrodes with coiled-spring prongs. Sensors, 2020, 20(17): 4733.
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27.	刘铁军, 张锐, 徐鹏. 基于运动想象的脑机接口关键技术研究. 中国生物医学工程学报, 2014, 33(6): 644-651.
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29.	许敏鹏, 程秀敏, 明东. 不同视觉注意状态调制稳态视觉诱发电位特征的可分性研究. 生物医学工程学杂志, 2019, 36(5): 705-710.
30.	Allison B, Luth T, Valbuena D, et al. BCI demographics: How many (and what kinds of) people can use an SSVEP BCI?. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(2): 107-116.
31.	Guger C, Daban S, Sellers E, et al. How many people are able to control a P300-based brain-computer interface (BCI)?. Neurosci Lett, 2009, 462: 94-98.
32.	Brunner P, Joshi S, Briskin S, et al. Does the ‘P300’ speller depend on eye gaze?. J Neural Eng, 2010, 7(5): 056013.
33.	Treder M S, Blankertz B. (C)overt attention and visual speller design in an ERP-based brain-computer interface. Behav Brain Funct, 2010, 6(1): 28.
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36.	Vidaurre C, Kawanabe M, et al. Toward unsupervised adaptation of LDA for brain-computer interfaces. IEEE Trans Biomed Eng, 2011, 58(3): 587-597.
37.	Faller J, Vidaurre C, Solis-Escalante T, et al. Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Trans Neural Syst Rehabil EngI, 2012, 20(3): 313-319.
38.	Josef F, Reinhold S, Ursula C, et al. A Co-adaptive brain-computer interface for end users with severe motor impairment. PLoS One, 2014, 9(7): e101168.
39.	Samek W, Meinecke F C, Müller K R. Transferring subspaces between subjects in brain-computer interfacing. IEEE Trans Biomed Eng, 2013, 60(8): 2289-2298.
40.	Perdikis S, Leeb R, Millan J D R. Subject-oriented training for motor imagery brain-computer interfaces// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Chicago: IEEE, 2014: 1259-1262.
41.	Kobler R J, Scherer R. Restricted Boltzmann machines in sensory motor rhythm brain-computer interfacing: A study on inter-subject transfer and co-adaptation// 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Budapest: IEEE, 2016: 469-474.
42.	杨晨. 面向应用的稳态视觉诱发电位脑-机接口算法及系统研究. 北京: 清华大学, 2018.
43.	Galán F, Nuttin M, Lew E, et al. A brain-actuated wheelchair: Asynchronous and non-invasive brain-computer interfaces for continuous control of robots. Clin Neurophysiol, 2008, 119(9): 2159-2169.
44.	Millan J, Renkens F, Mourino J, et al. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng, 2004, 51(6): 1026-1033.
45.	Müller K R, Blankertz B. Toward noninvasive brain–computer interfaces. IEEE Signal Process Mag, 2006, 23(5): 128.
46.	Tonin L, Leeb R, Tavella M, et al. The role of shared-control in BCI-based telepresence// 2010 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Istanbul: IEEE, 2010: 1462-1466.
47.	Williamson J, Murray-Smith R, Blankertz B, et al. Designing for uncertain, asymmetric control: Interaction design for brain–computer interfaces. Int J Hum-Comput St, 2009, 67(10): 827-841.
48.	Wills S A, Mackay D J C. Dasher-an efficient writing system for brain-computer interfaces?. IEEE Trans Neural Syst Rehabil EngI, 2006, 14(2): 244-246.
49.	Garipelli G, Galán F, Chavarriaga R, et al. The use of brain-computer interfacing in ambient intelligence. Constructing Ambient Intelligence, 2008, 11: 268-285.
50.	Kanemura A, Morales Y, Kawanabe M, et al. A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration// 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo: IEEE, 2013: 865-870.
51.	Liu Yaru, Liu Yadong, Tang Jingsheng, et al. A self-paced BCI prototype system based on the incorporation of an intelligent environment-understanding approach for rehabilitation hospital environmental control. Comput Biol Med, 2020, 118: 103618.
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55.	蒋梦蝶. 膝骨关节炎患者移动辅具使用现状及影响因素分析. 开封: 河南大学, 2020.
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57.	傅嘉豪, 焦学军, 曹勇, 等. 基于 EEG 的多因素认知任务脑力负荷研究. 航天医学与医学工程, 2020, 33(1): 35-44.
58.	Hart S G, Staveland L E. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Adv Psychol, 1988, 52: 139-183.
59.	Leeb R, Sagha H, Chavarriaga R, et al. A hybrid brain-computer interface based on the fusion of electroence phalographic and electromyographic activities. J Neural Eng, 2011, 8(2): 025011.
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61.	李红卫, 陈小刚. 基于高级控制策略的脑-机接口控制机械臂系统. 北京生物医学工程, 2019, 38(1): 36-41.
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1. 伏云发, 郭衍龙, 张夏冰, 等. 脑机接口: 变革性的人机交互. 北京: 国防工业出版社, 2020.
2. Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin: Springer Publishing Company, 2013.
3. 张旭, 袁芳, 姚兆林. 脑机接口: 电路与系统. 北京: 机械工业出版社, 2020.
4. Zjajo A. Brain-machine interface: Circuits and systems. Berlin: Springer Publishing Company, 2016.
5. 伏云发, 龚安民, 陈超, 等. 面向实用的脑-机接口: 缩小研究与实际应用之间的差距. 北京: 电子工业出版社, 2021.
6. Allison B Z, Dunne S, Leeb R, et al. Towards practical Brain-Computer Interfaces: Bridging the gap from research to real-world applications. Berlin: Springer Publishing Company, 2012.
7. Chavarriaga R, Fried-Oken M, Kleih S, et al. Heading for new shores! Overcoming pitfalls in BCI design. Brain Computer Interfaces, 2017, 4(1-2): 60-73.
8. Zickler C, Riccio A, Leotta F, et al. A brain-computer interface as input channel for a standard assistive technology software. Clin EEG Neurosci, 2011, 42(4): 236-244.
9. Riccio A, Pichiorri F, Schettini F, et al. Interfacing brain with computer to improve communication and rehabilitation after brain damage. Prog Brain Res, 2016, 228: 357-387.
10. Kübler A, Holz E M, Angela R, et al. The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications. PLoS One, 2014, 9(12): e112392.
11. 许为, 葛列众. 人因学发展的新取向. 心理科学进展, 2018, 26(9): 1521-1534.
12. 蒋祖华. 人因工程. 北京: 科学出版社, 2011.
13. Giulia L, Alessia P, Luca S, et al. Developing brain-computer interfaces from a user-centered perspective: Assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals. Appl Ergon, 2015, 50: 139-146.
14. Kübler A, Nijboer F, Kleih S. Hearing the needs of clinical users. Handb Clin Neurol, 2020, 168: 353-368.
15. Kübler A, Zickler C, Holz E, et al. Applying the user-centred design to evaluation of brain-computer interface controlled applications. Biomed Eng, 2013, 58(15): 3234-3234.
16. Abiri R, Borhani S, Kilmarx J, et al. A usability study of low-cost wireless brain-computer interface for cursor control using online linear model. IEEE Trans Hum Mach Syst, 2020, 50(4): 287-297.
17. Kübler A. The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome. Neuroethics, 2020, 13(3): 1-18.
18. Martin S, Armstrong E, Thomson E, et al. A qualitative study adopting a user-centered approach to design and validate a brain computer interface for cognitive rehabilitation for people with brain injury. Assist Technol, 2018, 30(5): 233-241.
19. Branco M P, Pels E G M, Sars R H, et al. Brain-computer interfaces for communication: Preferences of individuals with locked-in syndrome. Neurorehab Neural Re, 2021, 35(3): 267-279.
20. Wolpaw J R, Millán J D R, Ramsey N F. Brain-computer interfaces: Definitions and principles. Handb Clin Neurol, 2020, 168: 15-23.
21. 伏云发, 杨秋红, 徐宝磊, 等. 脑-机接口原理与实践. 北京: 国防工业出版社, 2017.
22. Wolpaw J R, Wolpaw E W. Brain-computer interfaces: Principles and practice. Oxford: Oxford University Press, 2012.
23. Benjamin B, Michael T, Carmen V, et al. The Berlin brain-computer interface: Non-medical uses of BCI technology. Front Neurosci, 2010, 4: 198.
24. 高久伟, 卢乾波, 郑璐, 等. 柔性生物电传感技术. 材料导报, 2020, 34(1): 1095-1106.
25. Kimura M, Nakatani S, Nishida S I, et al. 3D printable dry EEG electrodes with coiled-spring prongs. Sensors, 2020, 20(17): 4733.
26. Casson A J. Wearable EEG and beyond. Biomed Eng Lett, 2019, 1: 53-71.
27. 刘铁军, 张锐, 徐鹏. 基于运动想象的脑机接口关键技术研究. 中国生物医学工程学报, 2014, 33(6): 644-651.
28. Kübler A, Blankertz B, Müller K R, et al. A model of BCI control// Proceedings of the 5th International Brain-Computer Interface Conference 2011. Graz: Verlag der Technischen Universität Graz, 2011: 100-103.
29. 许敏鹏, 程秀敏, 明东. 不同视觉注意状态调制稳态视觉诱发电位特征的可分性研究. 生物医学工程学杂志, 2019, 36(5): 705-710.
30. Allison B, Luth T, Valbuena D, et al. BCI demographics: How many (and what kinds of) people can use an SSVEP BCI?. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(2): 107-116.
31. Guger C, Daban S, Sellers E, et al. How many people are able to control a P300-based brain-computer interface (BCI)?. Neurosci Lett, 2009, 462: 94-98.
32. Brunner P, Joshi S, Briskin S, et al. Does the ‘P300’ speller depend on eye gaze?. J Neural Eng, 2010, 7(5): 056013.
33. Treder M S, Blankertz B. (C)overt attention and visual speller design in an ERP-based brain-computer interface. Behav Brain Funct, 2010, 6(1): 28.
34. Alonso-Valerdi L M, Arreola-Villarruel M A, Argüello-García J. Brain-computer interfaces: Conceptualization, redesign challenges and social impact. Revista Mexicana de Ingenieria Biomedica, 2020, 40(3): 1-18.
35. Grübler G, Hildt E. Brain-computer interfaces in their ethical, social and cultural contexts. Berlin: Springer Publishing Company, 2014.
36. Vidaurre C, Kawanabe M, et al. Toward unsupervised adaptation of LDA for brain-computer interfaces. IEEE Trans Biomed Eng, 2011, 58(3): 587-597.
37. Faller J, Vidaurre C, Solis-Escalante T, et al. Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Trans Neural Syst Rehabil EngI, 2012, 20(3): 313-319.
38. Josef F, Reinhold S, Ursula C, et al. A Co-adaptive brain-computer interface for end users with severe motor impairment. PLoS One, 2014, 9(7): e101168.
39. Samek W, Meinecke F C, Müller K R. Transferring subspaces between subjects in brain-computer interfacing. IEEE Trans Biomed Eng, 2013, 60(8): 2289-2298.
40. Perdikis S, Leeb R, Millan J D R. Subject-oriented training for motor imagery brain-computer interfaces// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Chicago: IEEE, 2014: 1259-1262.
41. Kobler R J, Scherer R. Restricted Boltzmann machines in sensory motor rhythm brain-computer interfacing: A study on inter-subject transfer and co-adaptation// 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Budapest: IEEE, 2016: 469-474.
42. 杨晨. 面向应用的稳态视觉诱发电位脑-机接口算法及系统研究. 北京: 清华大学, 2018.
43. Galán F, Nuttin M, Lew E, et al. A brain-actuated wheelchair: Asynchronous and non-invasive brain-computer interfaces for continuous control of robots. Clin Neurophysiol, 2008, 119(9): 2159-2169.
44. Millan J, Renkens F, Mourino J, et al. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng, 2004, 51(6): 1026-1033.
45. Müller K R, Blankertz B. Toward noninvasive brain–computer interfaces. IEEE Signal Process Mag, 2006, 23(5): 128.
46. Tonin L, Leeb R, Tavella M, et al. The role of shared-control in BCI-based telepresence// 2010 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Istanbul: IEEE, 2010: 1462-1466.
47. Williamson J, Murray-Smith R, Blankertz B, et al. Designing for uncertain, asymmetric control: Interaction design for brain–computer interfaces. Int J Hum-Comput St, 2009, 67(10): 827-841.
48. Wills S A, Mackay D J C. Dasher-an efficient writing system for brain-computer interfaces?. IEEE Trans Neural Syst Rehabil EngI, 2006, 14(2): 244-246.
49. Garipelli G, Galán F, Chavarriaga R, et al. The use of brain-computer interfacing in ambient intelligence. Constructing Ambient Intelligence, 2008, 11: 268-285.
50. Kanemura A, Morales Y, Kawanabe M, et al. A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration// 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo: IEEE, 2013: 865-870.
51. Liu Yaru, Liu Yadong, Tang Jingsheng, et al. A self-paced BCI prototype system based on the incorporation of an intelligent environment-understanding approach for rehabilitation hospital environmental control. Comput Biol Med, 2020, 118: 103618.
52. 刘玉仁, 董震曜. 快速原型法在软件设计中的应用. 光电对抗与无源干扰, 2002(4): 6-9.
53. Zelkowitz M V. A case study in rapid prototyping. Software Pract Exper, 1980, 10(12): 1037-1042.
54. Zickler C, Donna V D, Kaiser V, et al. Brain computer interaction applications for people with disabilities: Defining user needs and user requirements// 10th European Conference for the Advancement of Assistive Technology. Florence: Association for the Advancement of Assistive Technology in Europe, 2009: 185-189.
55. 蒋梦蝶. 膝骨关节炎患者移动辅具使用现状及影响因素分析. 开封: 河南大学, 2020.
56. Colucci M, Tofani M, Trioschi D, et al. Reliability and validity of the Italian version of Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 (QUEST-IT 2.0) with users of mobility assistive device. Disabil Rehabil Assist Technol, 2019, 25: 1-4.
57. 傅嘉豪, 焦学军, 曹勇, 等. 基于 EEG 的多因素认知任务脑力负荷研究. 航天医学与医学工程, 2020, 33(1): 35-44.
58. Hart S G, Staveland L E. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Adv Psychol, 1988, 52: 139-183.
59. Leeb R, Sagha H, Chavarriaga R, et al. A hybrid brain-computer interface based on the fusion of electroence phalographic and electromyographic activities. J Neural Eng, 2011, 8(2): 025011.
60. Chai Xiaoke, Zhang Zhimin, Guan Kai, et al. A hybrid BCI-controlled smart home system combining SSVEP and EMG for individuals with paralysis. Biomed Signal Process Control, 2020, 56(2): 101687.
61. 李红卫, 陈小刚. 基于高级控制策略的脑-机接口控制机械臂系统. 北京生物医学工程, 2019, 38(1): 36-41.
62. 杨帮华, 刘丽, 陆文宇, 等. 基于虚拟现实技术的脑机交互反馈系统设计. 北京生物医学工程, 2011, 30(4): 401-404.
63. 瞿军. 基于生物电信号的人机交互技术及其在虚拟现实中的应用研究. 广州: 华南理工大学, 2019.
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Journal of Biomedical Engineering

Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology

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