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Human Eye Tracking Through Electro-Oculography (EOG): A Review

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Cooperative Design, Visualization, and Engineering (CDVE 2022)

Abstract

The basic principles and techniques used in Electrooculography (EOG) are presented. The main objective of this work is to present a state of art of Electrooculography (EOG) in Human computer Interface (HCI) to help researchers interested in the field.

This research was funded by FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación/\(_{-}\)Proyecto PGC2018-095709-B-C21 (AEI, FEDER, UE).

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References

  1. Abdel-Gawad, A.A., Ahmed, S.A., Abd El-Samie, F.E., Ayman, M.B.: Wireless Personal Communications Efficient Classification of Horizontal and Vertical EOG Signals for Human Computer Interaction (2021, under review). https://doi.org/10.21203/rs.3.rs-471385/v1

  2. Agustin, J.S., Mateo, J.C., Hansen, J.P., Villanueva, A.: Evaluation of the potential of gaze input for game interaction. PsychNol. J. 7, 213–236 (2009)

    Google Scholar 

  3. Barea, R., Boquete, L., López, E., Mazo, M.: Guidance of a wheelchair using electrooculography. In: Proceedings of the 3rd IMACS International Multiconference Circuits, Systems, Communications and Computers, Athens, Greece, 4–8 July 1999, pp. 2421–2426 (1999)

    Google Scholar 

  4. Barea, R., Boquete, L., Mazo, M., López, E.: Wheelchair guidance strategies using EOG. J. Intell. Robot. Syst. Theory Appl. 34, 279–299 (2002). https://doi.org/10.1023/A:1016359503796

    Article  MATH  Google Scholar 

  5. Barea, R., Boquete, L., Ortega, S., López, E., Rodríguez-Ascariz, J.M.: EOG-based eye movements codification for human computer interaction. Expert Syst. Appl. 39, 2677–2683 (2009). https://doi.org/10.1016/j.eswa.2011.08.123

    Article  Google Scholar 

  6. Beukelman, D., Fager, S., Nordness, A.: Communication support for people with ALS. Neurol. Res. Int. 2011, 714693 (2011)

    Article  Google Scholar 

  7. Borghetti, D., Bruni, A., Fabbrini, M., Murri, L., Sartucci, F.: A low-cost interface for control of computer functions by means of eye movements. Comput. Biol. Med. 37, 1765–1770 (2007)

    Article  Google Scholar 

  8. Bott, N.T., Lange, A., Rentz, D., Buffalo, E., Clopton, P., Zola, S.: Web camera based eye tracking to assess visual memory on a visual paired comparison task. Front. Neurosci. 11, 370 (2017). https://doi.org/10.3389/fnins.2017.00370

    Article  Google Scholar 

  9. Brown, M., Marmor, M., Vaegan, Zrenner, E., Brigell, M., Bach, M.: ISCEV standard for clinical electro-oculography (EOG) 2006. Doc. Ophthalmol. 113, 205–212 (2006). https://doi.org/10.1007/s10633-006-9030-0

  10. Bulling, A., Roggen, D., Tröster, G.: Wearable EOG goggles: seamless sensing and context-awareness in everyday environments. J. Ambient Intell. Smart Environ. 1, 157–171 (2009). https://doi.org/10.3233/AIS-2009-0020

    Article  Google Scholar 

  11. Bulling, A., Member, S., Ward, J.A., Gellersen, H., Tröster, G.: Eye movement analysis for activity recognition using electrooculography. IEEE Trans. Pattern Anal. Mach. Intell. 33, 741–753 (2011). https://doi.org/10.1109/TPAMI.2010.86

    Article  Google Scholar 

  12. Chang, W.-D., Cha, H.-S., Kim, D.Y., Kim, S.H., Im, C.-H.: Development of an electrooculogram-based eye-computer interface for communication of individuals with amyotrophic lateral sclerosis. J. Neuroeng. Rehabil. 14, 89 (2017)

    Article  Google Scholar 

  13. Chang, W.D.: Electrooculograms for human-computer interaction: a review. Sensors 19, 2690 (2019). https://doi.org/10.3390/s19122690

    Article  Google Scholar 

  14. Christensena, J.A.E., et al.: Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson’s and Parkinson’s disease. J. Neurosci. Methods 235, 262–276 (2014)

    Article  Google Scholar 

  15. Choudhari, A., Porwal, P., Meriaudeau, F.: An electrooculography based human machine interface for wheelchair control. Biocybern. Biomed. Eng. 39(3), 673–685 (2019). https://doi.org/10.1016/j.bbe.2019.04.002

    Article  Google Scholar 

  16. Deng, L.Y., Hsu, C.L., Lin, T.C., Tuan, J.S., Chang, S.M.: EOG-based human-computer interface system development. Expert Syst. Appl. 37(4), 3337–3343 (2010). https://doi.org/10.1016/j.eswa.2009.10.017

    Article  Google Scholar 

  17. Djanian, S.: Eye movement classification using deep learning. Master thesis, Aalborg University (2019)

    Google Scholar 

  18. Dorr, M., Bohme, M., Martinetz, T., Brath, E.: Gaze beats mouse: a case study. PsychNol. J. 7, 16–19 (2007)

    Google Scholar 

  19. Economu, S.G., Stefanis, C.N.: Electrooculographic (EOG) findings in manic-depressive illness. Acta Psychiatr. Scand. 60(2), 155–162 (1979). https://doi.org/10.1111/j.1600-0447.1979.tb03583.x

    Article  Google Scholar 

  20. Estrany, B., Fuster-Parra, P., Garcia, A., Luo, Y.: Human computer interface by EOG tracking. In: International Conference on Proceedings of the 1st ACM International Conference on PErvasive Technologies Related to Assistive Environments, Athens, Greece, pp. 1–9. ACM (2008). https://doi.org/10.1049/cp:20081109

  21. Estrany, B., Fuster-Parra, P., Garcia, A., Luo, Y.: Accurate interaction with computer by eye movement tracking. In: 2008 IET 4th International Conference on Intelligent Environments (IE08), pp. 1–7 (2008). https://doi.org/10.1145/1389586.1389694

  22. Estrany, B., Fuster-Parra, P., Garcia, A., Luo, Y.: EOG signal processing and analysis for controlling computer by eye movements. In: International Conference on Proceedings of the 2nd ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu, Greece, pp. 9–13. ACM (2009). https://doi.org/10.1145/1579114.1579132

  23. Fang, F., Shinozaki, T.: Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems. PLoS ONE 13, e0192684 (2018)

    Article  Google Scholar 

  24. Fountoulakis, K.N., Fotiou, F., Lacovides, A., Kaprinis, G.: Is there a dysfunction in the visual system of depressed patients? Ann. Gen. Psychiatry 4(7), 1–10 (2005)

    Google Scholar 

  25. Haslwanter, T., Clarke, A.H.: Eye movement measurement. Electro-oculography and video-oculography [Internet] 1st ed. Handbook of Clinical Neurophysiology. Elsevier B.V. (2010). https://doi.org/10.1016/S1567-4231(10)09005-2

  26. Hjorth, B., Elema-Schönander, A.B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29, 306–310 (1970). https://doi.org/10.1016/0013-4694(70)90143-4

    Article  Google Scholar 

  27. Hládek, L., Porr, B., Brimijoin, W.O.: Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography. PLoS ONE 13(1), e0190420 (2018). https://doi.org/10.1371/journal.pone.0190420

    Article  Google Scholar 

  28. Lam, R.W., Beattie, C.W., Buchanan, A., Remick, R.A., Zis, A.P.: Low electrooculographic ratios in patient with seasonal affective disorder. Am. J. Psychiatry 148(11), 1526–1529 (1991). https://doi.org/10.1176/ajp.148.11.1526

    Article  Google Scholar 

  29. Iáñez, E., Azorin, J.M., Perez-Vidal, C.: Using eye movement to control a computer: a design for a lightweight electro-oculogram electrode array and computer interface. PLoS ONE 8, 1–10 (2013). https://doi.org/10.1371/journal.pone.0067099. PMID: 23843986

    Article  Google Scholar 

  30. Itsuki, N., et al.: Improved method for measuring electrooculogram and its evaluation. In: Proceedings of IEEE Conference on Control, Automation, Robotics and Vision, pp. 947–952 (2004)

    Google Scholar 

  31. Kim, M.R., Yoon, G.: Control signal from EOG analysis and its application. Int. J. Electr. Comput. Electron. Commun. Eng. 7, 864–867 (2013)

    Google Scholar 

  32. Kumar, D., Poole, E.: Classification of EOG for human computer interface. In: Proceedings IEEE Conference on Engineering in Medicine and Biology Society, pp. 64–67 (2002)

    Google Scholar 

  33. Lee, K.-R., Chang, W.-D., Kim, S., Im, C.-H.: Real-time ‘eye-writing’ recognition using electrooculogram (EOG). IEEE Trans. Neural Syst. Rehabil. Eng. 25, 37–48 (2016)

    Article  Google Scholar 

  34. Hládek, L., Porr, B., Brimijoin, W.O.: Real-time estimation of horizontal gaze angle by saccade integration using in-ear electrooculography. PLoS ONE 13(1), e0190420 (2018). https://doi.org/10.1371/journal.pone.0190420

    Article  Google Scholar 

  35. Manabe, H., Fukumoto, M., Yagi, T.: Direct gaze estimation based on nonlinearity of EOG. IEEE Trans. Biomed. Eng. 62(6), 1553–1562 (2015). https://doi.org/10.1109/TBME.2015.2394409

    Article  Google Scholar 

  36. McPartland, R.J., Kupfer, D.J.: Computerised measures of electro-oculographic activity during sleep. Int. J. Biomed. Comput. 9, 409–419 (1978). https://doi.org/10.1016/0020-7101(78)90048-X

    Article  Google Scholar 

  37. Mowrer, O.H., Ruch, R.C., Miller, N.E.: The corneoretinal potencial difference as the basis of the galvanometric method of recording eye movements. Am. J. Physiol. 114, 423 (1936)

    Article  Google Scholar 

  38. Muensterer, O.J., Lacher, M., Zoeller, C., Bronstein, M., Kübler, J.: Google Glass in pediatric surgery: an exploratory study. Int. J. Surg. 12, 281–289 (2014)

    Article  Google Scholar 

  39. Nilsson, S., Gustafsson, T., Carleberg, P.: Hands free interaction with virtual information in a real environment. PsychNol. J. 7, 175–196 (2007)

    Google Scholar 

  40. Oh, S., Kumar, P.S., Kwon, H., Varadan, V.K.: Wireless brain-machine interface using EEG and EOG: brain wave classification. In: Proceedings of the Nanosensors, Biosensors, and Info-Tech Sensors and Systems, San Diego, CA, USA, 11–15 March 2012 (2012)

    Google Scholar 

  41. Perez Reynoso, F.D., et al.: A custom EOG-based HMI using neural network modeling to real-time for the trajectory tracking of a manipulator robot. Front. Neurorobot. 14, 1–23 (2020). Article 578834. https://doi.org/10.3389/fnbot.2020.578834

  42. Pettersson, K., Jagadeesan, S., Lukander, K., Henelius, A., Haeggström, E., Müller, K., et al.: Algorithm for automatic analysis of electro-oculographic data. Biomed. Eng. Online 12 (2013). https://doi.org/10.1186/1475-925X-12-110

  43. Ramli, R., Arof, H., Ibrahim, F., Mokhtar, N., Idris, M.Y.I.: Using finite state machine and a hybrid of EEG signal and EOG artifacts for an asynchronous wheelchair navigation. Expert Syst. Appl. 42, 2451–2463 (2015). https://doi.org/10.1016/j.eswa.2014.10.052

    Article  Google Scholar 

  44. Rusydi, M., Sasaki, M., Ito, S.: Affine transform to reform pixel coordinates of EOG signals for controlling robot manipulators using gaze motions. Sensors 14, 10107–10123 (2014)

    Article  Google Scholar 

  45. Ryu, J., Lee, M., Kim, D.H.: EOG-based eye tracking protocol using baseline drift removal algorithm for long-term eye movement detection. Expert Syst. Appl. 131, 275–287 (2019). https://doi.org/10.1016/j.eswa.2019.04.039

    Article  Google Scholar 

  46. Schott, E.: Über die Registrierung des Nystagmus und anderer Augenbewegungen vermittels des Seitengalvenometers. Deutches Archiv für Klinishe Medizin 140, 79–90 (1922)

    Google Scholar 

  47. Simini, F., Touya, A., Senatore, A., Pereira, J.: Gaze tracker by electrooculography (EOG) on a head-band. In: 2011 10th International Workshop on Biomedical Engineering, pp. 1–4 (2011). https://doi.org/10.1109/IWBE.2011.6079050

  48. Singh, H., Singh, J.: Human eye tracking and related issues: a review. Int. J. Sci. Res. 2(9), 1–10 (2012)

    Google Scholar 

  49. Tamura, H., Yan, M., Sakurai, K., Tanno, K.: EOG-sEMG human interface for communication. Comput. Intell. Neurosci. 2016, 1–11 (2016). Article ID 7354082. https://doi.org/10.1155/2016/7354082

  50. Toivanen, M., Pettersson, K., Lukander, K.: A probabilistic real-time algorithm for detecting blinks, saccades, and fixations from EOG data. J. Eye Mov. Res. 8, 1–14 (2015). https://doi.org/10.16910/jemr.8.2.1

    Article  Google Scholar 

  51. Tsai, J.-Z., Lee, C.-K., Wu, C.-M., Wu, J.-J., Kao, K.-P.: A feasibility study of an eye-writing system based on electro-oculography. J. Med. Biol. Eng. 28, 39–46 (2008)

    Google Scholar 

  52. Xiao, J., Qu, J., Li, Y.: An electrooculogram-based interaction method and its music-on-demand application in a virtual reality environment. IEEE Access 7, 22059–2207 (2019)

    Article  Google Scholar 

  53. Xu, J., et al.: Electrooculography and tactile perception collaborative interface for 3D human-machine interaction. ACS Nano 16, 6687–6699 (2022). https://doi.org/10.1021/acsnano.2c01310

    Article  Google Scholar 

  54. Yamagishi, K., Hori, J., Miyakawa, M.: Development of EOG-based communication system controlled by eight-directional eye movements. In: Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 30 August–3 September 2006, pp. 2574–2577 (2006)

    Google Scholar 

  55. Yan, M., Go, S., Tamura, H.: Communication system using EOG for persons with disabilities and its judgment by EEG. Artif. Life Robot. 19, 89–94 (2014)

    Article  Google Scholar 

  56. Young, L.R., Sheena, D.: Eye-movement measurement techniques. Am. Psychol. 30, 315–330 (1975)

    Article  Google Scholar 

  57. Yu, J.H., Lee, B.H., Kim, D.H.: EOG based eye movement measure of visual fatigue caused by 2D and 3D displays. In: Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 305–308 (2012). https://doi.org/10.1109/BHI.2012.6211573

  58. Zhang, J., Wang, B., Zhang, C., Xiao, Y., Wang, M.Y.: An EEG/EMG/EOG-based multimodal human-machine interface to real-time control of a soft robot hand. Front. Neurorobot. 13(7), 1–13 (2019)

    Google Scholar 

  59. Zoccolan, D., Graham, B., Cox, D.: A self-calibrating, camera-based eye tracker for the recording of rodent eye movements. Front. Neurosci. 4, 193 (2010). https://doi.org/10.3389/fnins.2010.00193

    Article  Google Scholar 

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Correspondence to Pilar Fuster-Parra .

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Estrany, B., Fuster-Parra, P. (2022). Human Eye Tracking Through Electro-Oculography (EOG): A Review. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2022. Lecture Notes in Computer Science, vol 13492. Springer, Cham. https://doi.org/10.1007/978-3-031-16538-2_8

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  • DOI: https://doi.org/10.1007/978-3-031-16538-2_8

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