Motor imagery task classification using transformation based features. (March 2017)

• Record Type: Journal Article
• Authors: Khorshidtalab, Aida; Salami, Momoh J.E.; Akmeliawati, Rini
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 213
• Page End: 219
• Publication Date: 2017-03
• Subjects: EEG -- Linear prediction coding -- QR decomposition -- Singular value decomposition -- Channel selection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.12.006
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A feature extraction method named as LP_QR is proposed. The proposed method is compared with LP_SVD. The comparison covers channel selection, time consumption and accuracy. This study is conducted on dataset BCI IIIa of four motor imagery movements. 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.