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

NIU Yafeng; WANG Jiahao; WU Jinchun; XUE Chengqi; YANG Wenjun

doi:10.11999/JEIT211040

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 455-463

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

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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

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

doi: 10.11999/JEIT211040

1.
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
2.
National Key Laboratory of Science and Technology on Aircraft Control, Xi’an 710018, China

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

Received Date: 2021-09-27
Accepted Date: 2022-01-20
Rev Recd Date: 2022-01-10

Available Online: 2022-01-21

Publish Date: 2022-02-25

Abstract

Abstract

For the SSVEP-BCI, an ergonomic study is carried out on the effects of stimulation interface element size and spacing on recognition efficiency and user experience. In this experiment, the red squares are used as the stimulation elements. The squares are located on the upper, lower, left and right positions. The independent variables include two factors: size and spacing. Factor 1 (size) is the side length of the square, which is divided into three levels: 100px, 150px, and 200px; Factor 2 (spacing) is the vertical/horizontal distance between the element center and the interface center, which is divided into three levels: 200px/400px, 300px/600px, and 400px/800px. The dependent variables are the completion time and the number of failures of the tasks. Subjective evaluation is carried out after the experiment. Based on ISO 9241 usability standard, Likert 7-point scale is used to score the participants’ satisfaction of the interfaces. The results of the ergonomics experiment show that the element size has a significant impact on the recognition efficiency, the stimulation element with side length of 200px has the highest recognition efficiency, while the element spacing has no impact. The subjective evaluation results show that element spacing has a significant impact on user satisfaction. The compactness (200px/400px) or alienation (400px/800px) of stimulating elements will lead to the decline of satisfaction. The satisfaction of 300px/600px spacing level is the best, while the size has no impact. From the perspective of design ergonomics, it is found that the size and spacing of stimulating interface elements have an impact on the efficiency of SSVEP-BCI system and user satisfaction respectively. The research conclusion has guiding and reference value for standardizing the design of SSVEP-BCI and improving the efficiency of SSVEP-BCI system.
- Brain Computer Interface(BCI),
- Steady-State Visual Evoked Potentials (SSVEP),
- Ergonomics,
- Size,
- Spacing

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References(43)

References

[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