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Motor imagery task classification using transformation based features
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DOI：10.1016/j.bspc.2016.12.006.png)
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Abstract
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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 Ilia 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. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
EEG
Linear prediction coding
QR decomposition
Singular value decomposition
Channel selection
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