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

Video：

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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.	張旭, 袁芳, 姚兆林. 腦機接口: 電路與系統. 北京: 機械工業出版社, 2020.
4.	Zjajo A. Brain-machine interface: Circuits and systems. Berlin: Springer Publishing Company, 2016.
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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.	許為, 葛列眾. 人因學發展的新取向. 心理科學進展, 2018, 26(9): 1521-1534.
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29.	許敏鵬, 程秀敏, 明東. 不同視覺注意狀態調制穩態視覺誘發電位特征的可分性研究. 生物醫學工程學雜志, 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.
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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.
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42.	楊晨. 面向應用的穩態視覺誘發電位腦-機接口算法及系統研究. 北京: 清華大學, 2018.
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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.
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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.
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1. 伏云發, 郭衍龍, 張夏冰, 等. 腦機接口: 變革性的人機交互. 北京: 國防工業出版社, 2020.
2. Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin: Springer Publishing Company, 2013.
3. 張旭, 袁芳, 姚兆林. 腦機接口: 電路與系統. 北京: 機械工業出版社, 2020.
4. Zjajo A. Brain-machine interface: Circuits and systems. Berlin: Springer Publishing Company, 2016.
5. 伏云發, 龔安民, 陳超, 等. 面向實用的腦-機接口: 縮小研究與實際應用之間的差距. 北京: 電子工業出版社, 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. 許為, 葛列眾. 人因學發展的新取向. 心理科學進展, 2018, 26(9): 1521-1534.
12. 蔣祖華. 人因工程. 北京: 科學出版社, 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. 伏云發, 楊秋紅, 徐寶磊, 等. 腦-機接口原理與實踐. 北京: 國防工業出版社, 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. 高久偉, 盧乾波, 鄭璐, 等. 柔性生物電傳感技術. 材料導報, 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. 劉鐵軍, 張銳, 徐鵬. 基于運動想象的腦機接口關鍵技術研究. 中國生物醫學工程學報, 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 Universit?t Graz, 2011: 100-103.
29. 許敏鵬, 程秀敏, 明東. 不同視覺注意狀態調制穩態視覺誘發電位特征的可分性研究. 生物醫學工程學雜志, 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. 楊晨. 面向應用的穩態視覺誘發電位腦-機接口算法及系統研究. 北京: 清華大學, 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. 劉玉仁, 董震曜. 快速原型法在軟件設計中的應用. 光電對抗與無源干擾, 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. 蔣夢蝶. 膝骨關節炎患者移動輔具使用現狀及影響因素分析. 開封: 河南大學, 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, 焦學軍, 曹勇, 等. 基于 EEG 的多因素認知任務腦力負荷研究. 航天醫學與醫學工程, 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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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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