Motor-imagery EEG signal classification using position matching and vector quantisation
by Tae-Ung Jang; Byeong Man Kim; Yeon-Mo Yang; Wansu Lim; Duk-Hwan Oh
International Journal of Telemedicine and Clinical Practices (IJTMCP), Vol. 1, No. 4, 2016

Abstract: This paper proposes a motor-imagery electroencephalograph (EEG) signal classification method using vector quantisation and position matching. An EEG signal is transformed into a sequence of feature vectors which are extracted from the signal by short-time Fourier transform (STFT). A sequence of feature vectors, in turn, is transformed into code index sequence using the code book which is created by vector quantisation through Linde-Buzo-Gray (LBG) algorithm. Test signals are classified by K-nearest neighbour (KNN) where similarity among vectors is obtained by position matching. Brain computer interface (BCI) competition 2003 dataset III was used in the proposed method to perform the classification test and the maximum performance of 83.57% was obtained.

Online publication date: Wed, 17-Aug-2016

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