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International Conference on Advanced Machine Learning Technologies and Applications

AMLTA 2019: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) pp 179–189Cite as

Analyzing Electrooculography (EOG) for Eye Movement Detection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

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.

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Author information

Authors and Affiliations

  1. Scientific Computing Department, Faculty of Computer and Information Science, FCIS-Ain Shams University, Cairo, Egypt

    Radwa Reda, Manal Tantawi, Howida shedeed & Mohamed F. Tolba

Authors
  1. Radwa Reda
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  2. Manal Tantawi
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  3. Howida shedeed
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  4. Mohamed F. Tolba
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Corresponding authors

Correspondence to Radwa Reda , Manal Tantawi , Howida shedeed or Mohamed F. Tolba .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Reda, R., Tantawi, M., shedeed, H., Tolba, M.F. (2020). Analyzing Electrooculography (EOG) for Eye Movement Detection. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_18

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