The classification of EEG signal processing using different machine learning techniques for BCI application

Brain-Computer Interface (BCI) or Human-Machine Interface is now becoming vital in biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cog...

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Main Authors:	Rashid, Mamunur, Norizam, Sulaiman, Mahfuzah, Mustafa, Sabira, Khatun, Bari, Bifta Sama
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer, Singapore 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/24498/ http://umpir.ump.edu.my/id/eprint/24498/ http://umpir.ump.edu.my/id/eprint/24498/1/29.%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf http://umpir.ump.edu.my/id/eprint/24498/2/29.1%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf

id	ump-24498
recordtype	eprints
spelling	ump-244982020-01-20T02:25:57Z http://umpir.ump.edu.my/id/eprint/24498/ The classification of EEG signal processing using different machine learning techniques for BCI application Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama TK Electrical engineering. Electronics Nuclear engineering Brain-Computer Interface (BCI) or Human-Machine Interface is now becoming vital in biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cognitive states to extract the suitable EEG features that can be em-ployed to control BCI devices which can be used by disabled or paralyzed people. The EEG features in term of power spectral density, spectral centroids, standard deviation and entropy are selected and investigated from two different mental exercises; i) quick solving math and ii) relax (do nothing). Then the se-lected features are classified using Linear Discriminant Analysis (LDA), Sup-port Vector Machine (SVM) and K-Nearest Neighbors (k-NN) classifier. Among all these features, the best accuracy has been achieved by the power spectral density. The accuracies of this feature are 95%, 100%, 100% with LDA, SVM and K-NN respectively. Finally, the translation algorithm will be con-structed using selected and classified EEG features to control the BCI devices. Springer, Singapore 2019-04 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24498/1/29.%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf pdf en http://umpir.ump.edu.my/id/eprint/24498/2/29.1%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf Rashid, Mamunur and Norizam, Sulaiman and Mahfuzah, Mustafa and Sabira, Khatun and Bari, Bifta Sama (2019) The classification of EEG signal processing using different machine learning techniques for BCI application. In: RiTA 2018: Robot Intelligence Technology and Applications, 16-18 December 2018 , Putrajaya, Selangor, Malaysia. pp. 207-221.. ISBN 978-981-13-7779-2 https://doi.org/10.1007/978-981-13-7780-8_17
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama The classification of EEG signal processing using different machine learning techniques for BCI application
description	Brain-Computer Interface (BCI) or Human-Machine Interface is now becoming vital in biomedical engineering and technology field which applying EEG technologies to provide assistive device technology (AT) to humans. Hence, this paper presents the results of analyzing EEG signals from various human cognitive states to extract the suitable EEG features that can be em-ployed to control BCI devices which can be used by disabled or paralyzed people. The EEG features in term of power spectral density, spectral centroids, standard deviation and entropy are selected and investigated from two different mental exercises; i) quick solving math and ii) relax (do nothing). Then the se-lected features are classified using Linear Discriminant Analysis (LDA), Sup-port Vector Machine (SVM) and K-Nearest Neighbors (k-NN) classifier. Among all these features, the best accuracy has been achieved by the power spectral density. The accuracies of this feature are 95%, 100%, 100% with LDA, SVM and K-NN respectively. Finally, the translation algorithm will be con-structed using selected and classified EEG features to control the BCI devices.
format	Conference or Workshop Item
author	Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama
author_facet	Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Sabira, Khatun Bari, Bifta Sama
author_sort	Rashid, Mamunur
title	The classification of EEG signal processing using different machine learning techniques for BCI application
title_short	The classification of EEG signal processing using different machine learning techniques for BCI application
title_full	The classification of EEG signal processing using different machine learning techniques for BCI application
title_fullStr	The classification of EEG signal processing using different machine learning techniques for BCI application
title_full_unstemmed	The classification of EEG signal processing using different machine learning techniques for BCI application
title_sort	classification of eeg signal processing using different machine learning techniques for bci application
publisher	Springer, Singapore
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24498/ http://umpir.ump.edu.my/id/eprint/24498/ http://umpir.ump.edu.my/id/eprint/24498/1/29.%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf http://umpir.ump.edu.my/id/eprint/24498/2/29.1%20The%20classification%20of%20EEG%20signal%20processing%20using%20different.pdf
first_indexed	2023-09-18T22:37:07Z
last_indexed	2023-09-18T22:37:07Z
_version_	1777416669281386496

The classification of EEG signal processing using different machine learning techniques for BCI application

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