Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children

Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children

Dyslexia diagnosis in children could not be performed in the absence of a specialist. This issue can be overcome with the use of advanced technology. Using convolution neural networks (CNN), the automatic classification of dyslexia from electroencephalogram (EEG) can be achieved. The role of the CNN...

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Published in:7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings
Main Author: Mansor W.; Ahmad Zainuddin A.Z.; Mohd Hanafi M.F.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152369913&doi=10.1109%2fIECBES54088.2022.10079531&partnerID=40&md5=5dc06644136703fe06b3b42c66114134
id 2-s2.0-85152369913
spelling 2-s2.0-85152369913
Mansor W.; Ahmad Zainuddin A.Z.; Mohd Hanafi M.F.
Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
2022
7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings


10.1109/IECBES54088.2022.10079531
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152369913&doi=10.1109%2fIECBES54088.2022.10079531&partnerID=40&md5=5dc06644136703fe06b3b42c66114134
Dyslexia diagnosis in children could not be performed in the absence of a specialist. This issue can be overcome with the use of advanced technology. Using convolution neural networks (CNN), the automatic classification of dyslexia from electroencephalogram (EEG) can be achieved. The role of the CNN and EEG in distinguishing dyslexia in children has not been explored. This study reveals the performance of CNN in recognising EEG signals of dyslexic and normal children using raw signals. The recorded EEG signals were passed through Short-Time Fourier Transform Analysis to transform the signals into images, which were then served as the input of the CNN. It was found that the CNN could recognise the EEG signals of dyslexic children with an accuracy of 80.9% and 72.1% using training and testing data, respectively. © 2022 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Mansor W.; Ahmad Zainuddin A.Z.; Mohd Hanafi M.F.
spellingShingle Mansor W.; Ahmad Zainuddin A.Z.; Mohd Hanafi M.F.
Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
author_facet Mansor W.; Ahmad Zainuddin A.Z.; Mohd Hanafi M.F.
author_sort Mansor W.; Ahmad Zainuddin A.Z.; Mohd Hanafi M.F.
title Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
title_short Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
title_full Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
title_fullStr Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
title_full_unstemmed Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
title_sort Convolution Neural Network Performance in Recognising EEG Signals of Dyslexic Children
publishDate 2022
container_title 7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/IECBES54088.2022.10079531
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152369913&doi=10.1109%2fIECBES54088.2022.10079531&partnerID=40&md5=5dc06644136703fe06b3b42c66114134
description Dyslexia diagnosis in children could not be performed in the absence of a specialist. This issue can be overcome with the use of advanced technology. Using convolution neural networks (CNN), the automatic classification of dyslexia from electroencephalogram (EEG) can be achieved. The role of the CNN and EEG in distinguishing dyslexia in children has not been explored. This study reveals the performance of CNN in recognising EEG signals of dyslexic and normal children using raw signals. The recorded EEG signals were passed through Short-Time Fourier Transform Analysis to transform the signals into images, which were then served as the input of the CNN. It was found that the CNN could recognise the EEG signals of dyslexic children with an accuracy of 80.9% and 72.1% using training and testing data, respectively. © 2022 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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