Motor imagery task classification using transformation based features

tThis paper proposes a feature extraction method named as LP QR, based on the decomposition of theLPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired byLP SVD and is tested in the context of motor imagery electroencephalogram. The extracted featur...

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Main Authors:	Khorshidtalab, Aida, Salami, Momoh Jimoh Eyiomika, Akmeliawati, Rini
Format:	Article
Language:	English English English
Published:	2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/53662/ http://irep.iium.edu.my/53662/ http://irep.iium.edu.my/53662/ http://irep.iium.edu.my/53662/1/2017_aida_biomedical.pdf http://irep.iium.edu.my/53662/7/53662_Motor%20imagery%20task%20classification%20using%20transformation%20based%20features_SCOPUS.pdf http://irep.iium.edu.my/53662/8/53662_Motor%20imagery%20task%20classification%20using%20transformation%20based%20features_WoS.pdf

id	iium-53662
recordtype	eprints
spelling	iium-536622018-03-14T02:03:38Z http://irep.iium.edu.my/53662/ Motor imagery task classification using transformation based features Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Akmeliawati, Rini TK Electrical engineering. Electronics Nuclear engineering tThis paper proposes a feature extraction method named as LP QR, based on the decomposition of theLPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired byLP SVD and is tested in the context of motor imagery electroencephalogram. The extracted features areclassified and benchmarked against extracted features of LP SVD method. The two applied methods arealso compared regarding the required execution time, which further highlights their respective meritsand demerits. This paper closely examines the contribution of EEG channels of these two informationextraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning thenature of the extracted information is presented. This study is conducted on the BCI IIIa competitiondatabase of four motor imagery movements. The obtained results indicate that the proposed method isthe better choice if simplicity is demanded. The investigation into the role of EEG channels reveals thatlevel of contribution each channel can be quite dissimilar for different feature extraction algorithms. 2017 Article PeerReviewed application/pdf en http://irep.iium.edu.my/53662/1/2017_aida_biomedical.pdf application/pdf en http://irep.iium.edu.my/53662/7/53662_Motor%20imagery%20task%20classification%20using%20transformation%20based%20features_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/53662/8/53662_Motor%20imagery%20task%20classification%20using%20transformation%20based%20features_WoS.pdf Khorshidtalab, Aida and Salami, Momoh Jimoh Eyiomika and Akmeliawati, Rini (2017) Motor imagery task classification using transformation based features. Biomedical Signal Processing and Control, 33. pp. 213-219. ISSN 1746-8094 http://doi.org/10.1016/j.bspc.2016.12.006 doi:10.1016/j.bspc.2016.12.006
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Akmeliawati, Rini Motor imagery task classification using transformation based features
description	tThis paper proposes a feature extraction method named as LP QR, based on the decomposition of theLPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired byLP SVD and is tested in the context of motor imagery electroencephalogram. The extracted features areclassified and benchmarked against extracted features of LP SVD method. The two applied methods arealso compared regarding the required execution time, which further highlights their respective meritsand demerits. This paper closely examines the contribution of EEG channels of these two informationextraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning thenature of the extracted information is presented. This study is conducted on the BCI IIIa competitiondatabase of four motor imagery movements. The obtained results indicate that the proposed method isthe better choice if simplicity is demanded. The investigation into the role of EEG channels reveals thatlevel of contribution each channel can be quite dissimilar for different feature extraction algorithms.
format	Article
author	Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Akmeliawati, Rini
author_facet	Khorshidtalab, Aida Salami, Momoh Jimoh Eyiomika Akmeliawati, Rini
author_sort	Khorshidtalab, Aida
title	Motor imagery task classification using transformation based features
title_short	Motor imagery task classification using transformation based features
title_full	Motor imagery task classification using transformation based features
title_fullStr	Motor imagery task classification using transformation based features
title_full_unstemmed	Motor imagery task classification using transformation based features
title_sort	motor imagery task classification using transformation based features
publishDate	2017
url	http://irep.iium.edu.my/53662/ http://irep.iium.edu.my/53662/ http://irep.iium.edu.my/53662/ http://irep.iium.edu.my/53662/1/2017_aida_biomedical.pdf http://irep.iium.edu.my/53662/7/53662_Motor%20imagery%20task%20classification%20using%20transformation%20based%20features_SCOPUS.pdf http://irep.iium.edu.my/53662/8/53662_Motor%20imagery%20task%20classification%20using%20transformation%20based%20features_WoS.pdf
first_indexed	2023-09-18T21:15:54Z
last_indexed	2023-09-18T21:15:54Z
_version_	1777411559909228544

Motor imagery task classification using transformation based features

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