Eye Movements Recognition Using Electrooculography Signals

  • Radwa RedaEmail author
  • Manal Tantawi
  • Howida Shedeed
  • Mohamed F. Tolba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Nowadays, the number of people who suffer from severe motor disabilities is increasing remarkably. This disability gradually prevents them from moving all their limbs and they can communicate with their external environment only through the movement of their eyes. Hence, Human Computer Interfaces (HCI) have come out to provide those people with new communication way based on detecting eye movements. Eye movements are recorded by Electrooculogram (EOG). These signals are captured by placing electrodes horizontally and vertically around the eyes. In this work, the five eye movements left, right, up, down and blinking are classified by investigating both horizontal and vertical EOG signals. Statistical and geometrical features are extracted from EOG signals after applying Discrete Wavelet Transform (DWT). Six classifiers are examined in this study using both horizontal and vertical EOG features. The experimental results show the superiority of Naïve Bayes classifier.


Electro-oculogram (EOG) signal Human Computer/Machine Interface (HCI/HMI) Naïve Bayes Discrete Wavelet Transform (DWT) 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Radwa Reda
    • 1
    Email author
  • Manal Tantawi
    • 1
  • Howida Shedeed
    • 1
  • Mohamed F. Tolba
    • 1
  1. 1.Scientific Computing Department, Faculty of Computer and Information Science, FCISAin Shams UniversityCairoEgypt

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