Detection of directional eye movements based on the electrooculogram signals through an artificial neural network. (August 2015)

• Record Type: Journal Article
• Authors: Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem
• Is Part Of: Chaos, solitons and fractals. Volume 77(2015)
• Page Start: 225
• Page End: 229
• Publication Date: 2015-08
• Subjects: Electrooculogram -- Artificial neural network -- Eye movements -- System modeling -- Movement range; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2015.05.033
• Languages: English
• ISSNs: 0960-0779

Abstract

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.