The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline

Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research inv...

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Autores principales:	Jothi Letchumy Mahendra Kumar, Mamunur Rashid, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Norizam Sulaiman, Rozita Jailani, Anwar P.P. Abdul Majeed
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	BCI CWT EEG Transfer Learning SVM Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/215e4a22e4a44d9bb222b0e5ef531f48
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id	oai:doaj.org-article:215e4a22e4a44d9bb222b0e5ef531f48
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spelling	oai:doaj.org-article:215e4a22e4a44d9bb222b0e5ef531f482021-11-30T04:16:34ZThe classification of EEG-based wink signals: A CWT-Transfer Learning pipeline2405-959510.1016/j.icte.2021.01.004https://doaj.org/article/215e4a22e4a44d9bb222b0e5ef531f482021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000047https://doaj.org/toc/2405-9595Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets.Jothi Letchumy Mahendra KumarMamunur RashidRabiu Muazu MusaMohd Azraai Mohd RazmanNorizam SulaimanRozita JailaniAnwar P.P. Abdul MajeedElsevierarticleBCICWTEEGTransfer LearningSVMInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 421-425 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	BCI CWT EEG Transfer Learning SVM Information technology T58.5-58.64
spellingShingle	BCI CWT EEG Transfer Learning SVM Information technology T58.5-58.64 Jothi Letchumy Mahendra Kumar Mamunur Rashid Rabiu Muazu Musa Mohd Azraai Mohd Razman Norizam Sulaiman Rozita Jailani Anwar P.P. Abdul Majeed The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
description	Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets.
format	article
author	Jothi Letchumy Mahendra Kumar Mamunur Rashid Rabiu Muazu Musa Mohd Azraai Mohd Razman Norizam Sulaiman Rozita Jailani Anwar P.P. Abdul Majeed
author_facet	Jothi Letchumy Mahendra Kumar Mamunur Rashid Rabiu Muazu Musa Mohd Azraai Mohd Razman Norizam Sulaiman Rozita Jailani Anwar P.P. Abdul Majeed
author_sort	Jothi Letchumy Mahendra Kumar
title	The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
title_short	The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
title_full	The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
title_fullStr	The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
title_full_unstemmed	The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
title_sort	classification of eeg-based wink signals: a cwt-transfer learning pipeline
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/215e4a22e4a44d9bb222b0e5ef531f48
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The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline

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