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基于稳态视觉诱发电位的脑机接口元素尺寸和间距工效学研究

牛亚峰 王佳浩 伍金春 薛澄岐 杨文骏

牛亚峰, 王佳浩, 伍金春, 薛澄岐, 杨文骏. 基于稳态视觉诱发电位的脑机接口元素尺寸和间距工效学研究[J]. 电子与信息学报, 2022, 44(2): 455-463. doi: 10.11999/JEIT211040
引用本文: 牛亚峰, 王佳浩, 伍金春, 薛澄岐, 杨文骏. 基于稳态视觉诱发电位的脑机接口元素尺寸和间距工效学研究[J]. 电子与信息学报, 2022, 44(2): 455-463. doi: 10.11999/JEIT211040
NIU Yafeng, WANG Jiahao, WU Jinchun, XUE Chengqi, YANG Wenjun. Ergonomic Study on Element Size and Spacing of Brain Computer Interface Based on SSVEP[J]. Journal of Electronics & Information Technology, 2022, 44(2): 455-463. doi: 10.11999/JEIT211040
Citation: NIU Yafeng, WANG Jiahao, WU Jinchun, XUE Chengqi, YANG Wenjun. Ergonomic Study on Element Size and Spacing of Brain Computer Interface Based on SSVEP[J]. Journal of Electronics & Information Technology, 2022, 44(2): 455-463. doi: 10.11999/JEIT211040

基于稳态视觉诱发电位的脑机接口元素尺寸和间距工效学研究

doi: 10.11999/JEIT211040
基金项目: 国家自然科学基金(71801037, 72171044, 71871056),航空科学基金(20200058069002),东南大学“至善青年学者”支持计划
详细信息
    作者简介:

    牛亚峰:男,1988年生,博士,副教授,研究方向为先进人机交互、设计工效学等

    王佳浩:男,1997年生,硕士生,研究方向为脑机接口、工效学

    伍金春:男,1995年生,博士生,研究方向为神经设计学、人机交互

    薛澄岐:男,1961年生,博士,教授,研究方向为先进人机交互、工效学评价等

    杨文骏:男,1987年生,博士,高级工程师,研究方向为人机交互

    通讯作者:

    牛亚峰 nyf@seu.edu.cn

  • 中图分类号: TN911.7; TP302

Ergonomic Study on Element Size and Spacing of Brain Computer Interface Based on SSVEP

Funds: The National Natural Science Foundation of China (71801037, 72171044, 71871056), Aerospace Science Foundation of China (20200058069002), Southeast University “Zhi Shan Qing Nian Xue Zhe” Support Program Funding
  • 摘要: 针对基于稳态视觉诱发电位(SSVEP)的脑机接口系统,该文开展了屏显刺激界面元素尺寸和间距对识别效率和用户体验影响的工效学实验研究。该工效学实验使用红色正方形作为频闪刺激元素,刺激元素位于上、下、左、右等4个位置,自变量包含尺寸和间距两个因素。因素1为尺寸即正方形边长,分为100px,150px,200px3个水平;因素2为间距即元素中心与界面中心的垂直/水平距离,分为200px/400px,300px/600px,400px/800px3个水平。因变量为任务的完成时和失败次数。实验后开展主观评价,基于ISO 9241可用性标准,使用李克特7分量表对界面的满意度进行评分。工效学实验结果显示:元素尺寸对识别效率有显著影响,边长尺寸为200px的刺激元素识别效率最高,元素间距对识别效率没有影响。主观评价结果显示:元素间距对用户满意度有显著影响,刺激元素的紧凑(200px/400px)或疏远(400px/800px)都会导致满意度的下降,300px/600px间距水平的满意度最好,尺寸对用户满意度没有显著影响。该研究从设计工效学角度出发,发现了刺激界面元素尺寸、间距分别对脑机接口系统效率、用户满意度具有影响,研究结论对于规范脑机接口界面设计,提升脑机接口系统效率有重要的指导意义和借鉴价值。
  • 图  1  实验场景

    图  2  刺激元素界面设计

    图  3  单个试次实验流程图

    图  4  不同尺寸水平的完成时箱线图

    图  5  不同尺寸水平的完成时平均值折线图

    图  6  不同尺寸水平的失败次数箱线图

    图  7  不同尺寸水平的失败次数折线图

    图  8  不同间距水平的舒适度评价结果折线图

    图  9  不同间距水平的可接受度评价结果折线图

    图  10  不同间距水平的满意度得分折线图

  • [1] 高上凯. 脑机接口的现状与未来[J]. 机器人产业, 2019(5): 38–44. doi: 10.3969/j.issn.2096-0182.2019.05.007

    GAO Shangkai. Current situation and future of BCI[J]. Robot Industry, 2019(5): 38–44. doi: 10.3969/j.issn.2096-0182.2019.05.007
    [2] LEE J, LEUNG V, LEE A H, et al. Neural recording and stimulation using wireless networks of microimplants[J]. Nature Electronics, 2021, 4(8): 604–614. doi: 10.1038/s41928-021-00631-8
    [3] AFANASENKAU D, KALININA D, LYAKHOVETSKII V, et al. Rapid prototyping of soft bioelectronic implants for use as neuromuscular interfaces[J]. Nature Biomedical Engineering, 2020, 4(10): 1010–1022. doi: 10.1038/s41551-020-00615-7
    [4] SANI O G, ABBASPOURAZAD H, WONG Y T, et al. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification[J]. Nature Neuroscience, 2021, 24(1): 140–149. doi: 10.1038/s41593-020-00733-0
    [5] 罗志增, 鲁先举, 周莹. 基于脑功能网络和样本熵的脑电信号特征提取[J]. 电子与信息学报, 2021, 43(2): 412–418. doi: 10.11999/JEIT191015

    LUO Zhizeng, LU Xianju, and ZHOU Ying. EEG feature extraction based on brain function network and sample entropy[J]. Journal of Electronics &Information Technology, 2021, 43(2): 412–418. doi: 10.11999/JEIT191015
    [6] 徐宝国, 宋爱国, 费树岷. 在线脑机接口中脑电信号的特征提取与分类方法[J]. 电子学报, 2011, 39(5): 1025–1030.

    XU Baoguo, SONG Aiguo, and FEI Shumin. Feature extraction and classification of EEG in online brain-computer interface[J]. Acta Electronica Sinica, 2011, 39(5): 1025–1030.
    [7] 吴明权, 李海峰, 马琳. 单通道脑电信号中眼电干扰的自动分离方法[J]. 电子与信息学报, 2015, 37(2): 367–372. doi: 10.11999/JEIT140602

    WU Mingquan, LI Haifeng, and MA Lin. Automatic electrooculogram separation method for single channel electroencephalogram signals[J]. Journal of Electronics &Information Technology, 2015, 37(2): 367–372. doi: 10.11999/JEIT140602
    [8] 高诺, 张慧, 高志栋, 等. 基于脑机接口技术的上下肢康复系统研究[J]. 生物医学工程研究, 2021, 40(2): 166–171. doi: 10.19529/j.cnki.1672-6278.2021.02.11

    GAO Nuo, ZHANG Hui, GAO Zhidong, et al. Research on upper and lower limb rehabilitation system based on brain - computer interface technology[J]. Journal of Biomedical Engineering Research, 2021, 40(2): 166–171. doi: 10.19529/j.cnki.1672-6278.2021.02.11
    [9] LI Hongqi, BI Luzheng, and YI Jingang. Sliding-mode nonlinear predictive control of brain-controlled mobile robots[J]. IEEE Transactions on Cybernetics, 2020: 1–3. doi: 10.1109/TCYB.2020.3031667
    [10] WILLETT F R, AVANSINO D T, HOCHBERG L R, et al. High-performance brain-to-text communication via handwriting[J]. Nature, 2021, 593(7858): 249–254. doi: 10.1038/s41586-021-03506-2
    [11] MOSES D A, METZGER S L, LIU J R, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria[J]. New England Journal of Medicine, 2021, 385(3): 217–227. doi: 10.1056/NEJMoa2027540
    [12] 杨俊, 马正敏, 沈韬, 等. 基于深度时空特征融合的多通道运动想象EEG解码方法[J]. 电子与信息学报, 2021, 43(1): 196–203. doi: 10.11999/JEIT190300

    YANG Jun, MA Zhengmin, SHEN Tao, et al. Multichannel MI-EEG feature decoding based on deep learning[J]. Journal of Electronics &Information Technology, 2021, 43(1): 196–203. doi: 10.11999/JEIT190300
    [13] 徐光华, 张锋, 王晶, 等. 面向智能轮椅脑机导航的高频组合编码稳态视觉诱发电位技术研究[J]. 机械工程学报, 2013, 49(6): 21–29. doi: 10.3901/JME.2013.06.021

    XU Guanghua, ZHANG Feng, WANG Jing, et al. Research on key technology on time series combination coding-based high-frequency SSVEP in intelligent wheelchair BCI navigation[J]. Journal of Mechanical Engineering, 2013, 49(6): 21–29. doi: 10.3901/JME.2013.06.021
    [14] 李佳宁, 蒲江波, 崔红岩, 等. 基于体感电刺激诱发P300的脑机接口[J]. 仪器仪表学报, 2017, 38(6): 1353–1360. doi: 10.19650/j.cnki.cjsi.2017.06.006

    LI Jianing, PU Jiangbo, CUI Hongyan, et al. Electrical somatosensory based P300 for a brain-computer interface system[J]. Chinese Journal of Scientific Instrument, 2017, 38(6): 1353–1360. doi: 10.19650/j.cnki.cjsi.2017.06.006
    [15] 潘家辉. 基于P300和SSVEP的高性能脑机接口及其应用研究[D]. [博士论文], 华南理工大学, 2014.

    PAN Jiahui. A study on P300 and SSVEP-based high-performance brain-computer interface and its application[D]. [Ph. D. dissertation], South China University of Technology, 2014.
    [16] YADAV D, YADAV S, and VEER K. A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges[J]. Journal of Neuroscience Methods, 2020, 346: 108918. doi: 10.1016/j.jneumeth.2020.108918
    [17] CECOTTI H. A self-paced and calibration-less SSVEP-based brain–computer interface speller[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(2): 127–133. doi: 10.1109/TNSRE.2009.2039594
    [18] WU Zhenghua, LAI Yongxiu, XIA Yang, et al. Stimulator selection in SSVEP-based BCI[J]. Medical Engineering & Physics, 2008, 30(8): 1079–1088. doi: 10.1016/j.medengphy.2008.01.004
    [19] ZHU Danhua, BIEGER J, MOLINA G G, et al. A survey of stimulation methods used in SSVEP-based BCIs[J]. Computational Intelligence and Neuroscience, 2010, 2010: 702357. doi: 10.1155/2010/702357
    [20] LALOR E C, KELLY S P, FINUCANE C, et al. Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment[J]. EURASIP Journal on Advances in Signal Processing, 2005, 2005(19): 706906. doi: 10.1155/ASP.2005.3156
    [21] LBONNET. SSVEP: Steady-state visual-evoked potentials[EB/OL]. http://openvibe.inria.fr/steady-state-visual-evoked-potentials. 2021.
    [22] HWANG H J, LIM J H, JUNG Y J, et al. Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard[J]. Journal of Neuroscience Methods, 2012, 208(1): 59–65. doi: 10.1016/j.jneumeth.2012.04.011
    [23] LEGÉNY J, VICIANA-ABAD R, and LÉCUYER A. Toward contextual SSVEP-based BCI controller: Smart activation of stimuli and control weighting[J]. IEEE Transactions on Computational Intelligence and AI in Games, 2013, 5(2): 111–116. doi: 10.1109/TCIAIG.2013.2252348
    [24] MARTINEZ P, BAKARDJIAN H, and CICHOCKI A. Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm[J]. Computational Intelligence and Neuroscience, 2007, 2007: 094561. doi: 10.1155/2007/94561
    [25] MAHMOOD M, MZURIKWAO D, KIM Y S, et al. Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm[J]. Nature Machine Intelligence, 2019, 1(9): 412–422. doi: 10.1038/s42256-019-0091-7
    [26] 任泓锦, 张超, 伏云发. 影响单眼SSVEP控制的视力因素研究[J]. 四川师范大学学报:自然科学版, 2021, 44(3): 419–426. doi: 10.3969/j.issn.1001-8395.2021.03.017

    REN Hongjin, ZHANG Chao, and FU Yunfa. Study on the factors influencing the visual control of single eye SSVEP cooperatively controlling[J]. Journal of Sichuan Normal University:Natural Science, 2021, 44(3): 419–426. doi: 10.3969/j.issn.1001-8395.2021.03.017
    [27] 李鹏海, 许敏鹏, 万柏坤, 等. 视觉诱发电位脑-机接口实验范式研究进展[J]. 仪器仪表学报, 2016, 37(10): 2340–2351. doi: 10.19650/j.cnki.cjsi.2016.10.022

    LI Penghai, XU Minpeng, WAN Baikun, et al. Review of experimental paradigms in brain-computer interface based on visual evoked potential[J]. Chinese Journal of Scientific Instrument, 2016, 37(10): 2340–2351. doi: 10.19650/j.cnki.cjsi.2016.10.022
    [28] 陈景霞, 郝为, 张鹏伟, 等. RSVP与SSVEP混合脑电信号刺激与多类事件检测[J]. 计算机工程与应用, 2020, 56(15): 132–139. doi: 10.3778/j.issn.1002-8331.1905-0099

    CHEN Jingxia, HAO Wei, ZHANG Pengwei, et al. RSVP & SSVEP hybrid EEG stimulation and multi-class event detection[J]. Computer Engineering and Applications, 2020, 56(15): 132–139. doi: 10.3778/j.issn.1002-8331.1905-0099
    [29] NIU Yafeng, ZUO Hongrui, YANG Xin, et al. Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects[J]. Human Factors and Ergonomics in Manufacturing & Service Industries, 2021, 31(3): 249–269. doi: 10.1002/hfm.20884
    [30] NG K B, BRADLEY A P, and CUNNINGTON R. Stimulus specificity of a steady-state visual-evoked potential-based brain–computer interface[J]. Journal of Neural Engineering, 2012, 9(3): 036008. doi: 10.1088/1741-2560/9/3/036008
    [31] RAVI A, PEARCE S, ZHANG Xin, et al. User-specific channel selection method to improve SSVEP BCI decoding robustness against variable inter-stimulus distance[C]. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), San Francisco, USA, 2019: 283–286.
    [32] DUSZYK A, BIERZYŃSKA M, RADZIKOWSKA Z, et al. Towards an optimization of stimulus parameters for brain-computer interfaces based on steady state visual evoked potentials[J]. PLoS One, 2014, 9(11): e112099. doi: 10.1371/journal.pone.0112099
    [33] RENARD Y, LOTTE F, GIBERT G, et al. OpenViBE: An open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments[J]. Presence:Teleoperators and Virtual Environments, 2010, 19(1): 35–53. doi: 10.1162/pres.19.1.35
    [34] LI Xiaodong, WANG Xiaojun, WONG Chiman, et al. Influence of stimuli color combination on online SSVEP-based BCI performance[C]. 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Tianjin, China, 2019: 1–5.
    [35] ZHANG Yu, MA Hehe, JIN Jing, et al. Adaptive strategy for time window length in SSVEP-based brain-computer interface[C]. International Conference on Mechatronics & Control. Jinzhou, China, 2014: 140–143.
    [36] BIN Guangyu, GAO Xiaorong, YAN Zheng, et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method[J]. Journal of Neural Engineering, 2009, 6(4): 046002. doi: 10.1088/1741-2560/6/4/046002
    [37] LYMEX. 人眼视觉特性(HVS)[EB/OL]. http://www.360doc.com/content/14/1126/14/20100466_428221946.shtml, 2021.
    [38] TROTT P, PEARCY M, RUSTON S, et al. Three-dimensional analysis of active cervical motion: the effect of age and gender[J]. Clinical Biomechanics, 1996, 11(4): 201–206. doi: 10.1016/0268-0033(95)00072-0
    [39] ZAMBALDE E P, JABLONSKI G, DE ALMEIDA M B, et al. Evaluation of the target positioning in a SSVEP-BCI[C]. XXVI Brazilian Congress on Biomedical Engineering, Armação de Buzios, Brazil, 2019: 581–587.
    [40] ISO. ISO 9241–210: 2010 Ergonomics of human-system interaction- Part 210: Human-centred design for interactive systems[S]. 2010.
    [41] 瞿珏, 朱帅, 王崴, 等. 自适应界面视觉搜索认知特性研究[J]. 电子学报, 2021, 49(2): 338–345. doi: 10.12263/DZXB.20190286

    QU Jue, ZHU Shuai, WANG Wei, et al. Research on visual search cognitive characteristics of adaptive interface[J]. Acta Electronica Sinica, 2021, 49(2): 338–345. doi: 10.12263/DZXB.20190286
    [42] 吴晓莉, 薛澄岐, GEDEON T, 等. 数字化监控任务界面中信息特征的视觉搜索实验[J]. 东南大学学报:自然科学版, 2018, 48(5): 807–814. doi: 10.3969/j.issn.1001-0505.2018.05.005

    WU Xiaoli, XUE Chengqi, GEDEON T, et al. Visual search on information features on digital task monitoring interface[J]. Journal of Southeast University:Natural Science Edition, 2018, 48(5): 807–814. doi: 10.3969/j.issn.1001-0505.2018.05.005
    [43] CHEN Yonghao, YANG Chen, YE Xiaochen, et al. Implementing a calibration-free SSVEP-based BCI system with 160 targets[J]. Journal of Neural Engineering, 2021, 18(4): 046094. doi: 10.1088/1741-2552/ac0bfa
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出版历程
  • 收稿日期:  2021-09-27
  • 修回日期:  2022-01-10
  • 录用日期:  2022-01-20
  • 网络出版日期:  2022-01-21
  • 刊出日期:  2022-02-25

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