A review on voluntary or involuntary eye movement classification methods based on electro-oculogram and their applications_Journal of Biomedical Engineering

Authors：

LIU Jiarong ¹ , WANG Linyao ² , WU Yingnian ³ ,  HE Qing ¹

1. School of Instrument Science and Opto Electronics Engineering, Beijing Information Science & Technology University, Beijing 100192, P. R. China;
2. School of Literature and Journalism, Hubei Engineering University, Xiaogan, Hubei 432000, P. R. China;
3. School of Automation, Beijing Information Science & Technology University, Beijing 100192, P. R. China;

Corresponding author：

Keywords：

Electro-oculogram; Eye movement classification; Voluntary eye movement; Eye-computer interaction

DOI：

10.7507/1001-5515.202108066

Video：

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The eye-computer interaction technology based on electro-oculogram provides the users with a convenient way to control the device, which has great social significance. However, the eye-computer interaction is often disturbed by the involuntary eye movements, resulting in misjudgment, affecting the users’ experience, and even causing danger in severe cases. Therefore, this paper starts from the basic concepts and principles of eye-computer interaction, sorts out the current mainstream classification methods of voluntary/involuntary eye movement, and analyzes the characteristics of each technology. The performance analysis is carried out in combination with specific application scenarios, and the problems to be solved are further summarized, which are expected to provide research references for researchers in related fields.

Citation： LIU Jiarong, WANG Linyao, WU Yingnian, HE Qing. A review on voluntary or involuntary eye movement classification methods based on electro-oculogram and their applications. Journal of Biomedical Engineering, 2022, 39(4): 833-840, 847. doi: 10.7507/1001-5515.202108066 Copy

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2.	陈玉英, 隋光远, 瞿彬. 自主控制眼跳:实验范式、神经机制和应用. 心理科学进展, 2008, 16(1): 154-162.
3.	李可洋. 基于LabVIEW的眼电监护系统设计. 呼和浩特: 内蒙古大学, 2012.
4.	Lee J S, Lee H J, Lee W K, et al. Development of online speller using non-contact blink detection glasses. Journal of Biomedical Engineering Research, 2015, 36(6): 283-290.
5.	Heo J, Yoon H, Park K S. A novel wearable forehead EOG measurement system for human computer interfaces. Sensors (Basel), 2017, 17(7): 1485.
6.	姜羽, 王连明. 基于Emotiv Epoc+的眼动信号采集与识别方法研究. 东北师大学报:自然科学版, 2019, 51(2): 59-64.
7.	Anuradha R, Rathi G, Graceselvarani A, et al. Real time electric light control using EOG signals//2021 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam: IEEE, 2021: 1-4.
8.	Septanto H, Prihatmanto A S, Indrayanto A. A computer cursor controlled by eye movements and voluntary eye winks using a single channel EOG//2009 International Conference on Electrical Engineering and Informatics, Selangor: IEEE, 2009(1): 117-120.
9.	Yildiz M, Ülkütaş H Ö. A new PC-based text entry system based on EOG coding. Advances in Human-Computer Interaction, 2018, 2018(2): 1-8.
10.	Yu Y, Liu Y, Yin E, et al. An asynchronous hybrid spelling approach based on EEG-EOG signals for Chinese character input. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(6): 1292-1302.
11.	Molina-Cantero A J, Lebrato-Vázquez C, Merino-Monge M, et al. Communication technologies based on voluntary blinks: assessment and design. IEEE Access, 2019, 7: 70770-70798.
12.	ÇETİNEL G, Sevda G Ü L, TİRYAKİ Z, et al. Move your wheelchair with your eyes. International Journal of Applied Mathematics Electronics and Computers, 2017, 1(Special Issue): 5-8.
13.	Xiao Jing, Qu Jun, Li Yuanqing. An electrooculogram-based interaction method and its music-on-demand application in a virtual reality environment. IEEE Access, 2019, 7: 22059-22070.
14.	Saravanakumar D, Reddy R. A brain computer interface based communication system using SSVEP and EOG. Procedia Comput Sci, 2020, 167(6): 2033-2042.
15.	唐秀雯, 李享运, 陈广源, 等. 基于眼电的字符输入系统. 计算机系统应用, 2020, 29(1): 93-98.
16.	Zhang Rui, He Shenghong, Yang Xinghua, et al. An EOG-based human–machine interface to control a smart home environment for patients with severe spinal cord injuries. IEEE Trans Biomed Eng, 2019, 66(1): 89-100.
17.	Jalilifard A, Chen D, Mutasim A K, et al. Use of spontaneous blinking for application in human authentication. Engineering Science and Technology, an International Journal, 2020, 23(4): 903-910.
18.	Nakanishi M, Mitsukura Y. Wheelchair control system by using electrooculogram signal processing//The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Incheon: IEEE, 2013: 137-142.
19.	Choudhari A M, Porwal P, Jonnalagedda V, et al. An electrooculography based human machine interface for wheelchair control. Biocybern Biomed Eng, 2019, 39(3): 673-685.
20.	Ding X J, Lv Z. Design and development of an EOG-based simplified Chinese eye-writing system. Biomedical Signal Processing and Control, 2020, 57(4): 101767.
21.	陈振东, 杨飞帆, 刘惠鹏. 融合眼电及头部姿态的智能轮椅控制. 计算机测量与控制, 2019, 27(11): 74-78.
22.	Kabir A, Shahin F B, Islam M K. Design and implementation of an EOG-based mouse cursor control for application in human-computer interaction. J Phys Conf Ser, 2020, 1487(1): 012043.
23.	Ravichandran T, Kamel N, Al-Ezzi A A, et al. Electrooculography-based eye movement classification using deep learning models//2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Langkawi: IEEE, 2021: 57-61.
24.	Ma J, Zhang Y, Cichocki A, et al. A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control. IEEE Trans Biomed Eng, 2015, 62(3): 876-889.
25.	Saravanakumar D, Reddy M R. A virtual speller system using SSVEP and electrooculogram. Advanced Engineering Informatics, 2020, 44: 101059.
26.	丁晓娟. 融合EOG与视频的扫视眼动信号识别及应用研究. 合肥: 安徽大学, 2019.
27.	Kim D Y, Han C H, Im C H. Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients. Sci Rep, 2018, 8(1): 9505.
28.	Bhuyain M F, Kabir Shawon M , Sakib N, et al. Design and development of an EOG-based system to control electric wheelchair for people suffering from quadriplegia or quadriparesis//2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka: IEEE, 2019: 460-465.
29.	Huang Q, Zhang Z, Yu T, et al. An EEG-/EOG-based hybrid brain-computer interface: application on controlling an integrated wheelchair robotic arm system. Frontiers in neuroscience, 2019, 13: 1243.
30.	Lahane P, Adavadkar S P, Tendulkar S V, et al. Innovative approach to control wheelchair for disabled people using BCI//2018 3rd International Conference for Convergence in Technology (I2CT), Pune: IEEE, 2018: 1-5.
31.	Ka Hou H, K.g. S. Low-cost wireless electrooculography speller//2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki: IEEE, 2018: 123-128.
32.	Kim B J, Kwon K C, Yang Y M, et al. Keyboard for virtual reality head mounted display using electro-oculogram. The Journal of the Korea Contents Association, 2018, 18(1): 1-9.
33.	石和兵. 基于迁移学习的眼电分类研究与应用. 广州: 华南理工大学, 2019.
34.	尧德中. 脑-机接口: 如何从实验室到现实场景?. 生物医学工程学杂志, 2021, 38(3): 405-408.
35.	秦云, 刘铁军, 尧德中. 脑器交互学——脑与外界协同的新学科. 生物医学工程学杂志, 2021, 38(3): 507-511.
36.	Munteanu M, Magda A, Moga R, et al. EOG based controll of a graphical interface system for assisting paraplegic patients//2019 8th International Conference on Modern Power Systems (MPS), Cluj Napoca: IEEE, 2019: 1-4.
37.	He Shenghong, Zhou Yajun, Yu Tianyou, et al. EEG- and EOG-based asynchronous hybrid BCI: a system integrating a speller, a web browser, an E-mail client, and a file explorer. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(2): 519-530.
38.	陈卫东, 李昕, 刘俊, 等. 基于数学形态学的眼电信号识别及其应用. 浙江大学学报:工学版, 2011, 45(4): 644-649.
39.	Zhang J, Wang B, Zhang C, et al. Volitional and real-time control cursor based on eye movement decoding using a linear decoding model. Comput Intell Neurosci, 2016, 2016(5): 4069790.

1. 中国国家统计局. 中国统计年鉴—2020. (2020-09-23) [2021-07-30]. http: //www.stats.gov.cn/tjsj/ndsj/2020/indexch.htm.
2. 陈玉英, 隋光远, 瞿彬. 自主控制眼跳:实验范式、神经机制和应用. 心理科学进展, 2008, 16(1): 154-162.
3. 李可洋. 基于LabVIEW的眼电监护系统设计. 呼和浩特: 内蒙古大学, 2012.
4. Lee J S, Lee H J, Lee W K, et al. Development of online speller using non-contact blink detection glasses. Journal of Biomedical Engineering Research, 2015, 36(6): 283-290.
5. Heo J, Yoon H, Park K S. A novel wearable forehead EOG measurement system for human computer interfaces. Sensors (Basel), 2017, 17(7): 1485.
6. 姜羽, 王连明. 基于Emotiv Epoc+的眼动信号采集与识别方法研究. 东北师大学报:自然科学版, 2019, 51(2): 59-64.
7. Anuradha R, Rathi G, Graceselvarani A, et al. Real time electric light control using EOG signals//2021 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam: IEEE, 2021: 1-4.
8. Septanto H, Prihatmanto A S, Indrayanto A. A computer cursor controlled by eye movements and voluntary eye winks using a single channel EOG//2009 International Conference on Electrical Engineering and Informatics, Selangor: IEEE, 2009(1): 117-120.
9. Yildiz M, Ülkütaş H Ö. A new PC-based text entry system based on EOG coding. Advances in Human-Computer Interaction, 2018, 2018(2): 1-8.
10. Yu Y, Liu Y, Yin E, et al. An asynchronous hybrid spelling approach based on EEG-EOG signals for Chinese character input. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(6): 1292-1302.
11. Molina-Cantero A J, Lebrato-Vázquez C, Merino-Monge M, et al. Communication technologies based on voluntary blinks: assessment and design. IEEE Access, 2019, 7: 70770-70798.
12. ÇETİNEL G, Sevda G Ü L, TİRYAKİ Z, et al. Move your wheelchair with your eyes. International Journal of Applied Mathematics Electronics and Computers, 2017, 1(Special Issue): 5-8.
13. Xiao Jing, Qu Jun, Li Yuanqing. An electrooculogram-based interaction method and its music-on-demand application in a virtual reality environment. IEEE Access, 2019, 7: 22059-22070.
14. Saravanakumar D, Reddy R. A brain computer interface based communication system using SSVEP and EOG. Procedia Comput Sci, 2020, 167(6): 2033-2042.
15. 唐秀雯, 李享运, 陈广源, 等. 基于眼电的字符输入系统. 计算机系统应用, 2020, 29(1): 93-98.
16. Zhang Rui, He Shenghong, Yang Xinghua, et al. An EOG-based human–machine interface to control a smart home environment for patients with severe spinal cord injuries. IEEE Trans Biomed Eng, 2019, 66(1): 89-100.
17. Jalilifard A, Chen D, Mutasim A K, et al. Use of spontaneous blinking for application in human authentication. Engineering Science and Technology, an International Journal, 2020, 23(4): 903-910.
18. Nakanishi M, Mitsukura Y. Wheelchair control system by using electrooculogram signal processing//The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Incheon: IEEE, 2013: 137-142.
19. Choudhari A M, Porwal P, Jonnalagedda V, et al. An electrooculography based human machine interface for wheelchair control. Biocybern Biomed Eng, 2019, 39(3): 673-685.
20. Ding X J, Lv Z. Design and development of an EOG-based simplified Chinese eye-writing system. Biomedical Signal Processing and Control, 2020, 57(4): 101767.
21. 陈振东, 杨飞帆, 刘惠鹏. 融合眼电及头部姿态的智能轮椅控制. 计算机测量与控制, 2019, 27(11): 74-78.
22. Kabir A, Shahin F B, Islam M K. Design and implementation of an EOG-based mouse cursor control for application in human-computer interaction. J Phys Conf Ser, 2020, 1487(1): 012043.
23. Ravichandran T, Kamel N, Al-Ezzi A A, et al. Electrooculography-based eye movement classification using deep learning models//2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Langkawi: IEEE, 2021: 57-61.
24. Ma J, Zhang Y, Cichocki A, et al. A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control. IEEE Trans Biomed Eng, 2015, 62(3): 876-889.
25. Saravanakumar D, Reddy M R. A virtual speller system using SSVEP and electrooculogram. Advanced Engineering Informatics, 2020, 44: 101059.
26. 丁晓娟. 融合EOG与视频的扫视眼动信号识别及应用研究. 合肥: 安徽大学, 2019.
27. Kim D Y, Han C H, Im C H. Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients. Sci Rep, 2018, 8(1): 9505.
28. Bhuyain M F, Kabir Shawon M , Sakib N, et al. Design and development of an EOG-based system to control electric wheelchair for people suffering from quadriplegia or quadriparesis//2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka: IEEE, 2019: 460-465.
29. Huang Q, Zhang Z, Yu T, et al. An EEG-/EOG-based hybrid brain-computer interface: application on controlling an integrated wheelchair robotic arm system. Frontiers in neuroscience, 2019, 13: 1243.
30. Lahane P, Adavadkar S P, Tendulkar S V, et al. Innovative approach to control wheelchair for disabled people using BCI//2018 3rd International Conference for Convergence in Technology (I2CT), Pune: IEEE, 2018: 1-5.
31. Ka Hou H, K.g. S. Low-cost wireless electrooculography speller//2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki: IEEE, 2018: 123-128.
32. Kim B J, Kwon K C, Yang Y M, et al. Keyboard for virtual reality head mounted display using electro-oculogram. The Journal of the Korea Contents Association, 2018, 18(1): 1-9.
33. 石和兵. 基于迁移学习的眼电分类研究与应用. 广州: 华南理工大学, 2019.
34. 尧德中. 脑-机接口: 如何从实验室到现实场景?. 生物医学工程学杂志, 2021, 38(3): 405-408.
35. 秦云, 刘铁军, 尧德中. 脑器交互学——脑与外界协同的新学科. 生物医学工程学杂志, 2021, 38(3): 507-511.
36. Munteanu M, Magda A, Moga R, et al. EOG based controll of a graphical interface system for assisting paraplegic patients//2019 8th International Conference on Modern Power Systems (MPS), Cluj Napoca: IEEE, 2019: 1-4.
37. He Shenghong, Zhou Yajun, Yu Tianyou, et al. EEG- and EOG-based asynchronous hybrid BCI: a system integrating a speller, a web browser, an E-mail client, and a file explorer. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(2): 519-530.
38. 陈卫东, 李昕, 刘俊, 等. 基于数学形态学的眼电信号识别及其应用. 浙江大学学报:工学版, 2011, 45(4): 644-649.
39. Zhang J, Wang B, Zhang C, et al. Volitional and real-time control cursor based on eye movement decoding using a linear decoding model. Comput Intell Neurosci, 2016, 2016(5): 4069790.

Journal of Biomedical Engineering

A review on voluntary or involuntary eye movement classification methods based on electro-oculogram and their applications

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