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

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. © 2015 Elsevier Ltd. All rights reserved.

Eser Adı
[dc.title]
Detection of directional eye movements based on the electrooculogram signals through an artificial neural network
Yazar
[dc.contributor.author]
Erkaymaz, Hande
Yazar
[dc.contributor.author]
Özer, Mahmut
Yazar
[dc.contributor.author]
Orak, İlhami Muharrem
Yayın Yılı
[dc.date.issued]
2015
Yayıncı
[dc.publisher]
Elsevier Ltd
Yayın Türü
[dc.type]
article
Özet
[dc.description.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. © 2015 Elsevier Ltd. All rights reserved.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Artificial neural network
Konu Başlıkları
[dc.subject]
Electrooculogram
Konu Başlıkları
[dc.subject]
Eye movements
Konu Başlıkları
[dc.subject]
Movement range
Konu Başlıkları
[dc.subject]
System modeling
Künye
[dc.identifier.citation]
Erkaymaz, H., Ozer, M. ve Orak, İ. M. (2015). Detection of directional eye movements based on the electrooculogram signals through an artificial neural network. Chaos, Solitons & Fractals, 77, 225–229. doi:https://doi.org/10.1016/j.chaos.2015.05.033
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0960-0779
İlk Sayfa Sayısı
[dc.identifier.startpage]
225
Son Sayfa Sayısı
[dc.identifier.endpage]
229
Dergi Adı
[dc.relation.journal]
Chaos, Solitons and Fractals
Dergi Cilt Bilgisi
[dc.identifier.volume]
77
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.chaos.2015.05.033
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/5029
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
32
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
23 Şubat 2024 20:32
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Tıklayınız
movements signals electrooculogram detection artificial neural network directional features compute approach develop feed-forwarded movement proposed results reserved rights Elsevier accurately suggest determine become potential inputs involves intelligent important extracting information Therefore propose involving addition conventional
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