Fouziah Md YassinNorita Md NorwawiNor Azila NohAfishah AliasSofina Tamam2024-07-132024-07-1320242024-6-26Fouziah Md Yassin , Norita Md Norwawi , Nor Azila Noh , Afishah Alias4 and Sofina Tamam A Fusion Of Discrete Wavelet Transform-based And Time-domain Feature Extraction For Motor Imagery Classification. (2024). Jordanian Journal of Computers and Information Technology (JJCIT), 10(2), 108–122.2415-1076823-38https://oarep.usim.edu.my/handle/123456789/20583Jordanian Journal of Computers and Information Technology (JJCIT), Volume 10 Issue 2 Page (108–122)A motor imagery (MI)-based brain-computer interface (BCI) has performed successfully as a control mechanism with multiple electroencephalogram (EEG) channels. For practicality, fewer EEG channels are preferable. This paper investigates a single-channel EEG signal for MI. However, there are insufficient features that can be extracted due to a single-channel EEG signal being used in one region of the brain. An effective feature extraction technique plays a critical role in overcoming this limitation. Therefore, this study proposes a fusion of discrete wavelet transform (DWT)-based and time-domain feature extraction to provide more relevant information for classification. The highest accuracy obtained on the BCI Competition III (IVa) dataset is 87.5% with logistic regression (LR) while the OpenBMI dataset attained the highest accuracy of 93% with support vector machine (SVM) as the classifier. Addressing the potential of enhancing the performance of a single EEG channel located on the forehead, the achieved result is relatively promising.en-USMotor imageryFeature extractionElectroencephalogram (EEG)Discrete wavelet transformBrain-computer interface.text::journal::journal article108122102 (June)