Motor imagery task classification using transformation based features

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dc.contributor.authorKhorshidtalab, A.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.contributor.authorAkmeliawati, Rini
dc.date.accessioned2019-08-14T15:05:59Z
dc.date.available2019-08-14T15:05:59Z
dc.date.issued2017-03-01
dc.description.abstractThis paper proposes a feature extraction method named as LP QR, based on the decomposition of the LPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired by LP SVD and is tested in the context of motor imagery electroencephalogram. The extracted features are classified and benchmarked against extracted features of LP SVD method. The two applied methods are also compared regarding the required execution time, which further highlights their respective merits and demerits. This paper closely examines the contribution of EEG channels of these two information extraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning the nature of the extracted information is presented. This study is conducted on the BCI IIIa competition database of four motor imagery movements. The obtained results indicate that the proposed method is the better choice if simplicity is demanded. The investigation into the role of EEG channels reveals that level of contribution each channel can be quite dissimilar for different feature extraction algorithms.en_US
dc.identifier.citationKhorshidtalab, A., Salami, M. J., & Akmeliawati, R. (2017). Motor imagery task classification using transformation based features. Biomedical Signal Processing and Control, 33, 213-219.en_US
dc.identifier.issn1746-8094
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/491
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEEGen_US
dc.subjectLinear prediction codingen_US
dc.subjectQR decompositionen_US
dc.subjectSingular value decompositionen_US
dc.subjectChannel selectionen_US
dc.titleMotor imagery task classification using transformation based featuresen_US
dc.typeArticleen_US
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