Cover Image

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...

Full description

Saved in:
Bibliographic Details
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/53662/1/2017_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
http://irep.iium.edu.my/53662/
http://doi.org/10.1016/j.bspc.2016.12.006
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.53662
record_format dspace
spelling my.iium.irep.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 REM 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
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
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/53662/1/2017_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
http://irep.iium.edu.my/53662/
http://doi.org/10.1016/j.bspc.2016.12.006
_version_ 1643614391503945728
score 13.211869

Similar Items