%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 Curvelet Transform Based Two Class Motor Imagery Classification
	%U https://publications.waset.org/pdf/10012370
	%V 181
	%X One of the important parts of the brain-computer interface (BCI) studies is the classification of motor imagery (MI) obtained by electroencephalography (EEG). The major goal is to provide non-muscular communication and control via assistive technologies to people with severe motor disorders so that they can communicate with the outside world. In this study, an EEG signal classification approach based on multiscale and multi-resolution transform method is presented. The proposed approach is used to decompose the EEG signal containing motor image information (right- and left-hand movement imagery). The decomposition process is performed using curvelet transform which is a multiscale and multiresolution analysis method, and the transform output was evaluated as feature data. The obtained feature set is subjected to feature selection process to obtain the most effective ones using t-test methods. SVM and k-NN algorithms are assigned for classification.
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