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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)

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.

Keywords

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

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Copyright information

© 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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