Analyzing Electrooculography (EOG) for Eye Movement Detection

  • Radwa RedaEmail author
  • Manal TantawiEmail author
  • Howida shedeedEmail author
  • Mohamed F. TolbaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Although the cognitive parts of their brains are intact, some individual scan only interact with the outside environment through eye movements. Those people suffer from severe motor disabilities preventing them from moving all their limbs. Recently, Human Computer Interfaces (HCI) has emerged to help these people by providing them a new way for communication. These interfaces are based on detecting eye movements. Electro-oculogram (EOG) records eye movements through few electrodes placed around the eyes vertically and horizontally. In this paper, EOG vertical and horizontal signals are analyzed to detect four eye movements (left, right, up and down) along with blinking. Three statistical features are extracted from filtered EOG signals. Extracted features from horizontal and vertical EOG signals are concatenated to form final feature vector. K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), Decision Trees and Support Vector Machines (SVM) are six classifiers that are evaluated in this study. The results reveal the superiority of SVM Classifier in providing the best average accuracy.


Electro-oculogram (EOG) Human computer interface Statistical features Support Vector Machine 


  1. 1.
    Mitchell, J.D., Borasio, G.D.: Amyotrophic lateral sclerosis. Lancet 369, 2031–2041 (2007)CrossRefGoogle Scholar
  2. 2.
    Shaw, P.J.: Molecular and cellular pathways of neurodegeneration in motor neurone disease. J. Neurol. Neurosurg. Psychiatry 76, 1046–1057 (2005)CrossRefGoogle Scholar
  3. 3.
    Beleza-Meireles, A., Al-Chalabi, A.: Genetic studies of amyotrophic lateral sclerosis: controversies and perspectives. Amyotroph Lateral Scler. 10(1–14), 27 (2009)Google Scholar
  4. 4.
    Dion, P.A., Daoud, H., Rouleau, G.A.: Genetics of motor neuron disorders: new insights into pathogenic mechanisms. Nat. Rev. Genet. 10, 769–782 (2009)CrossRefGoogle Scholar
  5. 5.
    Alonso, A., Logroscino, G., Hernan, M.A.: Smoking and the risk of amyotrophic lateral sclerosis: a systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry 81(11), 1249–1252 (2010)CrossRefGoogle Scholar
  6. 6.
    Wang, H., O’reilly, E.J., Weisskopf, M.G., et al.: Smoking and risk of amyotrophic lateral sclerosis: a pooled analysis of 5 prospective cohorts. Arch. Neurol. 68, 207–213 (2011)Google Scholar
  7. 7.
    Miller, R.G., Mitchell, J.D., Moore, D.H.: Riluzole for amyotrophic lateral sclerosis (ALS)/motor neuron disease (MND). Cochrane Database Syst. Rev. 3, CD001447 (2012)Google Scholar
  8. 8.
    Sejvar, J.J., Baughman, A.L., Wise, M., Morgan, O.W.: Population incidence of Guillain-Barré syndrome: a systematic review and meta-analysis. Neuroepidemiology. 36(2), 123–133 (2011). Accessed 04 Dec 2014CrossRefGoogle Scholar
  9. 9.
    Milo, R., Kahana, E.: Multiple sclerosis: geoepidemiology, genetics and the environment. Autoimmun. Rev. 9(5), A387–A394 (2010)CrossRefGoogle Scholar
  10. 10.
    Majaranta, P.; Räihä, K.-J.: Twenty years of eye typing: Systems and design issues. In: Proceedings of the 2002 symposium on Eye tracking research & applications, pp. 15–22. ACM (2002)Google Scholar
  11. 11.
    Webster, J.: Medical Instrumentation: Application and Design. Wiley, Hoboken (2009)Google Scholar
  12. 12.
    Lv, Z., Wu, X.P, Li, M., Zhang, D.X.: Development of a human computer Interface system using EOG. Ministry of Education China, of Anhui University, Hefei, Anhui (2009)Google Scholar
  13. 13.
    Ya, T.O., Asumi, M.K.: Development of an input operation for the amyotrophic lateral sclerosis communication tool utilizing EOG. Med. Biol. Eng. 43(1), 172–178 (2005). (in Japanese)Google Scholar
  14. 14.
    Usakli, A.B., Gurkan, S.: Design of a novel efficient human–computer interfacean electrooculagram based virtual keyboard. IEEE Trans. Instrum. Meas. 59, 2099–2108 (2010)CrossRefGoogle Scholar
  15. 15.
    Deng, L.Y., Hsu, C.L., Lin, TCh., Tuan, J.S., Chang, S.M.: EOG based human-computer interface system development. Int. J. Expert Syst. Appl. 37(4), 3337–3343 (2010)CrossRefGoogle Scholar
  16. 16.
    Merino, M., Rivera, O., Gomez, I., Molina, A., Dorrenzoro, E.: A method of EOG signal processing to detect the direction of eye movements. In: First International Conference of Sensor Device Technologies and Applications, Italy. pp. 100–105, July 18–25 (2010)Google Scholar
  17. 17.
    Nathan, D.S., Vinod. A.P., Thomas, P.K.: An electrooculogram based assistive communication system with improved speed and accuracy using multi-directional eye movements. In: 35th International Conference on Telecommunications and Signal Processing (TSP), pp. 554–558, Prague, Czech Republic (2012)Google Scholar
  18. 18.
    Usakli, A.B., Gurkan, S., Aloise, F., Vecchiato, G., Babiloni, F.: On the use of electrooculogram for efficient human computer interfaces. Comput. Intell. Neurosci. (2010)Google Scholar
  19. 19.
    Aungsakul, S., et al.: Evaluating feature extraction methods of electroocculography (EOG) signal for human-computer interface. In: Proceedings, 3rd International Science, Social Science, Engineering and Energy Conference, Nakhon Pathom, vol. 32, pp. 246–252, Thailand (2012)Google Scholar
  20. 20.
    Aungsakul, S., et al.: Robust eye movement recognition using EOG signal for human-computer interface. In: Proceedings, 2nd International Conference on Software Engineering and Computer Systems, Kuantan, vol. 180, pp. 714–723, Malaysia (2011)Google Scholar
  21. 21.
    Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)MathSciNetGoogle Scholar
  22. 22.
    Johnson, A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle River (1988)zbMATHGoogle Scholar
  23. 23.
    Greene, W.H.: Econometric Analysis, 7th (edn.), pp. 803–806. Pearson Education, Boston (2012). ISBN 978-0-273-75356-8Google Scholar
  24. 24.
    Bishop, C.M.: Pattern Recognition and Machine Learning, pp. 206–209. Springer, New York (2006)Google Scholar
  25. 25.
    Mozina, M., Demsar, J., Kattan, M., Zupan, B.: Nomograms for visualization of naive bayesian classifier (PDF). In: Proceedings of PKDD 2004, pp. 337–348 (2004)Google Scholar
  26. 26.
    Quinlan, J.R.: Simplifying decision trees. Int. J. Man-Mach. Stud. 27(3), 221 (1987)CrossRefGoogle Scholar
  27. 27.
    Byun, H., Lee, S.-W.: Applications of support vector machines for pattern recognition: a survey. In: Pattern recognition with support vector machines, pp. 571–591(2002)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Scientific Computing Department, Faculty of Computer and Information ScienceFCIS-Ain Shams UniversityCairoEgypt

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