https://jjcit.org/paper/219 10.5455/jjcit.71-1700410729 Fouziah Md Yassin,Norita Md Norwawi,Nor Azila Noh,Afishah Alias,Sofina Tamam Motor imagery,Feature extraction,Electroencephalogram (EEG),Discrete wavelet transform,Brain-computer interface 3 3293 359 20-Nov.-2023 1-Feb.-2024 16-Feb.-2024 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.