Motor imagery task classification using transformation based features
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Date
2017-03-01
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
This 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.
Description
Keywords
EEG, Linear prediction coding, QR decomposition, Singular value decomposition, Channel selection
Citation
Khorshidtalab, A., Salami, M. J., & Akmeliawati, R. (2017). Motor imagery task classification using transformation based features. Biomedical Signal Processing and Control, 33, 213-219.