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Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

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  • Erkaymaz, Hande
  • Ozer, Mahmut
  • Orak, İlhami Muharrem

Abstract

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately.

Suggested Citation

  • Erkaymaz, Hande & Ozer, Mahmut & Orak, İlhami Muharrem, 2015. "Detection of directional eye movements based on the electrooculogram signals through an artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 225-229.
    • Handle: RePEc:eee:chsofr:v:77:y:2015:i:c:p:225-229
    DOI: 10.1016/j.chaos.2015.05.033
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    Cited by:

    1. Kaya, Ceren & Erkaymaz, Okan & Ayar, Orhan & Özer, Mahmut, 2018. "Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 164-174.
    2. Latifoğlu, Fatma & İleri, Ramis & Demirci, Esra, 2021. "Assessment of dyslexic children with EOG signals: Determining retrieving words/re-reading and skipping lines using convolutional neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).

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