Numerical study on adjusting parameters to improve gaze estimation using planar approximations from electro-oculogram signal voltage ratios

  • Fumihiko IshidaEmail author
  • Koki Wakata
Original Article


Gaze or eye movements are used as a communication interface in daily life. Herein, we developed a simple method for gaze estimations based on planar approximations of voltage ratios calculated from multiple electro-oculogram signals not affected by drift phenomena, which decrease accuracy. Subsequently, we conducted simulations using an eyeball battery model and investigated the effects of adjusting electrode arrangements, determination coefficients for planar approximations of voltage ratios, and threshold values for angles between simultaneous linear equations, to improve the estimation accuracy and decreased the number of required electrodes. Numerical experiments were used to identify arrangements of six electrodes with errors that were approximately 5° less than those of nine-electrode L-shaped arrangements, indicating improved estimation accuracy with fewer electrodes.


Gaze estimation Electro-oculography Planar approximation Voltage ratio Numerical experiment 



We would like to thank Editage ( for English language editing.


This study was supported by JSPS Grant-in Aid for Scientific Research 16K00364.

Compliance with ethical standards

Conflict of interest

We have no conflicts of interest or relationship with any companies or commercial organizations.

Ethical approval

This study was approved by the research ethics committee of the National Institute of Technology, Toyama College (No. 2).

Human and animal rights

This article does not contain any studies with human participants performed by any of the authors.


  1. 1.
    Ishida F, Hirano H, Fujimura Y. Development of a method for gaze estimation on the basis of planar approximations of the voltage ratio calculated from multiple electro-oculogram signals. Adv Biomed Eng. 2015;4:21–6.CrossRefGoogle Scholar
  2. 2.
    Gips J, Dimattia P, Curran FX, Olivieri P. Using EagleEyes—an electrodes based device for con-trolling the computer with your eyes—to help people with special needs. In: Proc 5th Int Conf Comput helping people with special needs. I; 1996. p. 77–83.Google Scholar
  3. 3.
    Tomita Y, Igarashi Y, Honda S, Matsuo N. Electro-oculography mouse for amyotrophic lateral sclerosis patients. Proc IEEE EMBS. 1996;5:1780–1.Google Scholar
  4. 4.
    Yagi Y, Koga K, Miyanaga A, Numata H, Funase A, Mukai T. R&D of eye-gaze interface: from basic research to commercialization. In: 44th Annual Conf Jpn Soc Med Biol Eng. OS21-6; 2005. p. 208.Google Scholar
  5. 5.
    Yagi T. Eye-gaze interfaces using electro-oculography (EOG). In: Proc 2010 workshop on eye gaze in intelligent human machine interaction; 2010. p. 28–32.Google Scholar
  6. 6.
    Tsai JZ, Lee CK, Wu CM, Wu JJ, Kao KP. A feasibility study of an eye-writing system based on electro-oculography. J Med Biol Eng. 2008;28(1):39–46.Google Scholar
  7. 7.
    Chang WD, Cha HS, Kim DY, Kim SH, Im CH. Development of an electrooculogram-based eye-computer interface for communication of individuals with amyotrophic lateral sclerosis. J Neuroeng Rehabil. 2017;14(1):89.CrossRefGoogle Scholar
  8. 8.
    Yan M, Tamura H, Tanno K. A study on gaze estimation system using cross-channels electrooculogram signals. Int Multiconf Eng Comput Sci. 2014;1:112–6.Google Scholar
  9. 9.
    Kumar D, Poole E. Classification of EOG for human computer interface. In: Proc Sec Jt EMBS-BMES Conf; 2002. p. 64–67.Google Scholar
  10. 10.
    Miyashita H, Hayashi M, Okada K. Implementation of EOG-based gaze estimation in HMD with head-tracker. In: Proc 18th Int Conf Artif Real and Telexistence; 2008. p. 20–27.Google Scholar
  11. 11.
    Eduardo I, Jose MA, Carlos PV. Using eye movement to control a computer: a design for a light-weight electro-oculogram electrode array and computer interface. PLoS ONE. 2013;8(7):e67099.CrossRefGoogle Scholar
  12. 12.
    Hori J, Chiba S. Development of EOG-based letter input interface on hierarchical screen keyboard considering the characteristics of eye movements. Far East J Electron Commun. 2015;14:53–69.CrossRefGoogle Scholar
  13. 13.
    Bulling A, Ward JA, Gellersen H, Troster G. Eye movement analysis for activity recognition using electrooculography. IEEE Trans Pattern Anal Mach Intell. 2011;33(4):741–53.CrossRefGoogle Scholar
  14. 14.
    Manabe H, Fukumoto M, Yagi T. Direct gaze estimation based on nonlinearity of EOG. IEEE Trans Biomed Eng. 2015;62:1553–62.CrossRefGoogle Scholar
  15. 15.
    Chang WD, Cha HS, Im CH. Removing the interdependency between horizontal and vertical eye-movement components in electrooculograms. Sensors. 2016;16(2):227.CrossRefGoogle Scholar
  16. 16.
    Fujimura Y, Ishida F. Development of gaze estimation method based on voltage-ratio calculated from multi EOG signals. BME Symp. 2012;2012:233–7.Google Scholar
  17. 17.
    Itsuki N, Kubo M, Shiraishi S, Nishikawa Y, Mimura Y. A battery model of the eyeball to calculate standing potential of the eye. J Jpn Ophthalmol. Soc. 1995;99:1012–6 (in Japanese).Google Scholar

Copyright information

© Korean Society of Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Electrical and Control Systems EngineeringNational Institute of Technology, Toyama CollegeToyamaJapan

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