eprintid: 15121
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/51/21
datestamp: 2023-11-10 03:29:44
lastmod: 2023-11-10 03:29:44
status_changed: 2023-11-10 01:58:41
type: conference_item
metadata_visibility: show
creators_name: Ravichandran, T.
creators_name: Kamel, N.
creators_name: Al-Ezzi, A.A.
creators_name: Alsaih, K.
creators_name: Yahya, N.
title: Electrooculography-based Eye Movement Classification using Deep Learning Models
ispublished: pub
keywords: Biomedical engineering; Biomedical signal processing; Brain; Convolutional neural networks; Deep learning; Deep neural networks; Long short-term memory; Motion analysis; Muscle; Neurodegenerative diseases; Neurons, ALS patients; Amyotrophic lateral sclerosis; Classification technique; Electro-oculogram; Eye movement classifications; Learning models; Motor neuron disease; Vertical direction, Eye movements
note: cited By 8; Conference of 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020 ; Conference Date: 1 March 2021 Through 3 March 2021; Conference Code:168430
abstract: Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND), is a specific disease that causes the death of neurons controlling voluntary muscles. Most ALS patients eventually lose the ability to walk, use their hands, speak, swallow, and breathe. In this paper, we use the electrooculogram (EOG) signals captured using four sensors placed on the controlling muscles of the eye movement in horizontal and vertical directions to classify four different eye movements. The classifier output is used to control a wheelchair, or any other device developed to help ALS patients in performing their daily needs. Contrary to the classical classification techniques where features are extracted first from the EOG signals, then used with a trained classifier, in this paper the EOG signals are fed directly into two deep neural networks using, respectively, the long-short term memory (LSTM) and the convolutional neural network (CNN). The results show an accuracy of 88.33 for the LSTM network and 90.3 for the CNN network in eye movement classification. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104878381&doi=10.1109%2fIECBES48179.2021.9398730&partnerID=40&md5=fd28c0d776c49406dbbb2703df87a8bc
id_number: 10.1109/IECBES48179.2021.9398730
full_text_status: none
publication: Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020
pagerange: 57-61
refereed: TRUE
isbn: 9781728142456
citation:   Ravichandran, T. and Kamel, N. and Al-Ezzi, A.A. and Alsaih, K. and Yahya, N.  (2021) Electrooculography-based Eye Movement Classification using Deep Learning Models.  In: UNSPECIFIED.