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Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation

<italic>Goal:</italic> Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without com...

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Main Authors: Alwasiti, H., Yusoff, M.Z.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:http://scholars.utp.edu.my/id/eprint/33889/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141601510&doi=10.1109%2fOJEMB.2022.3220150&partnerID=40&md5=7008622fa0e2cf117d6d10a68fe70539
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record_format eprints
spelling oai:scholars.utp.edu.my:338892022-12-20T03:45:06Z http://scholars.utp.edu.my/id/eprint/33889/ Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation Alwasiti, H. Yusoff, M.Z. <italic>Goal:</italic> Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. <italic>Methods:</italic> A customized Convolutional Neural Network with mixup augmentation was trained with <inline-formula><tex-math notation="LaTeX">\scriptstyle \mathtt ∼ </tex-math></inline-formula>120 EEG trials for only one subject per model. <italic>Results:</italic> Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95&#x0025; Confidence Interval: 0.908, 0.933) and 0.933 (95&#x0025; Confidence Interval: 0.922, 0.945) classification accuracy, respectively. <italic>Conclusions:</italic> We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work. Author Institute of Electrical and Electronics Engineers Inc. 2022 Article NonPeerReviewed Alwasiti, H. and Yusoff, M.Z. (2022) Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation. IEEE Open Journal of Vehicular Technology. pp. 1-8. ISSN 26441330 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141601510&doi=10.1109%2fOJEMB.2022.3220150&partnerID=40&md5=7008622fa0e2cf117d6d10a68fe70539 10.1109/OJEMB.2022.3220150 10.1109/OJEMB.2022.3220150 10.1109/OJEMB.2022.3220150
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description <italic>Goal:</italic> Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. <italic>Methods:</italic> A customized Convolutional Neural Network with mixup augmentation was trained with <inline-formula><tex-math notation="LaTeX">\scriptstyle \mathtt ∼ </tex-math></inline-formula>120 EEG trials for only one subject per model. <italic>Results:</italic> Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95&#x0025; Confidence Interval: 0.908, 0.933) and 0.933 (95&#x0025; Confidence Interval: 0.922, 0.945) classification accuracy, respectively. <italic>Conclusions:</italic> We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work. Author
format Article
author Alwasiti, H.
Yusoff, M.Z.
spellingShingle Alwasiti, H.
Yusoff, M.Z.
Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
author_facet Alwasiti, H.
Yusoff, M.Z.
author_sort Alwasiti, H.
title Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
title_short Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
title_full Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
title_fullStr Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
title_full_unstemmed Motor Imagery Classification for Brain Computer Interface using Deep Convolutional Neural Networks and Mixup Augmentation
title_sort motor imagery classification for brain computer interface using deep convolutional neural networks and mixup augmentation
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/33889/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141601510&doi=10.1109%2fOJEMB.2022.3220150&partnerID=40&md5=7008622fa0e2cf117d6d10a68fe70539
_version_ 1753790750285365248
score 13.211869

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