WASET
	%0 Journal Article
	%A Nebi Gedik
	%D 2022
	%J International Journal of Computer and Systems Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 181, 2022
	%T Wave Atom Transform Based Two Class Motor Imagery Classification
	%U https://publications.waset.org/pdf/10012369
	%V 181
	%X Electroencephalography (EEG) investigations of the brain computer interfaces are based on the electrical signals resulting from neural activities in the brain. In this paper, it is offered a method for classifying motor imagery EEG signals. The suggested method classifies EEG signals into two classes using the wave atom transform, and the transform coefficients are assessed, creating the feature set. Classification is done with SVM and k-NN algorithms with and without feature selection. For feature selection t-test approaches are utilized. A test of the approach is performed on the BCI competition III dataset IIIa.
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