Developing a method for classifying electro-oculography (EOG) signals using deep learning
Joint Authors
Tulbah, Muhammad F.
Rida, Radawi
Tantawi, Manal
Shadid, Huwayda A.
Source
International Journal of Intelligent Computing and Information Sciences
Issue
Vol. 22, Issue 3 (31 Aug. 2022), pp.1-13, 13 p.
Publisher
Ain Shams University Faculty of Computer and Information Sciences
Publication Date
2022-08-31
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Recently, a significant increase appears in the number of patients with severe motor disabilities even though the cognitive parts of their brains are intact.
these disabilities prevent them from being able to move all their limbs except for the movement of their eyes.
this creates great difficulty in carrying out the simplest daily activities, as well as difficulty in communicating with their surrounding environment.
with the advent of human computer interfaces (HCI), a new method of communication has been found based on determining the direction of eye movement.
the eye movement is recorded by Electro-oculogram (EOG) using a set of electrodes placed around the eye horizontally and vertically.
in this work, the horizontal and vertical EOG signals are filtered and analyzed to determine six eye movement directions (right, left, up, down, center, and double blinking).
the deep learning models namely Residual network and ResNet-50 network have been examined.
the experimental results show that the ResNet-50 network gives the best average accuracy 95.8%.
American Psychological Association (APA)
Rida, Radawi& Tantawi, Manal& Shadid, Huwayda A.& Tulbah, Muhammad F.. 2022. Developing a method for classifying electro-oculography (EOG) signals using deep learning. International Journal of Intelligent Computing and Information Sciences،Vol. 22, no. 3, pp.1-13.
https://search.emarefa.net/detail/BIM-1409072
Modern Language Association (MLA)
Rida, Radawi…[et al.]. Developing a method for classifying electro-oculography (EOG) signals using deep learning. International Journal of Intelligent Computing and Information Sciences Vol. 22, no. 3 (Aug. 2022), pp.1-13.
https://search.emarefa.net/detail/BIM-1409072
American Medical Association (AMA)
Rida, Radawi& Tantawi, Manal& Shadid, Huwayda A.& Tulbah, Muhammad F.. Developing a method for classifying electro-oculography (EOG) signals using deep learning. International Journal of Intelligent Computing and Information Sciences. 2022. Vol. 22, no. 3, pp.1-13.
https://search.emarefa.net/detail/BIM-1409072
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 12-13
Record ID
BIM-1409072