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Motor imagery task classification using transformation based features

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

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Abstract

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.

Item Type: Article (Journal)
Additional Information: 2470/53662
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Prof. Dr. Rini Akmeliawati
Date Deposited: 01 Jan 2017 22:40
Last Modified: 14 Mar 2018 10:03
URI: http://irep.iium.edu.my/id/eprint/53662

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