%0 Journal Article
	%A Nebi Gedik
	%D 2022
	%J International Journal of Health and Medical Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 181, 2022
	%T Two Class Motor Imagery Classification via Wave Atom Sub-Bants
	%U https://publications.waset.org/pdf/10012368
	%V 181
	%X The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.
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