Ahmad, Isah SalimZhang, ShuaiSaminu, SaniIsselmouMusa, Jamilu MaarufJavaid, ImranKAMHI, SOUHAKULSUM, UMMAY2022-01-102022-01-102021-05-25https://uilspace.unilorin.edu.ng/handle/20.500.12484/7308Motor imagery based on brain-computer interface (BCI) has aĨracted important research aĨention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalogram (EEG) signals to study brain activity with leě and right-hand movement. Deep learning (DL) has been employed for motor imagery (MI). In this article, a deep neural network (DNN) is proposed for classiėcation of leě and right movement of EEG signal using Common Spatial PaĨern (CSP) as feature extraction with standard gradient descent (GD) with momentum and adaptive learning rate LR. (GDMLR), the performance is compared using a confusion matrix, the average classiėcation accuracy is 87%, which is improved as compared with state-of-the-art methods that used different datasets.enBrain-computer interfaceElectroencephalographyMotor imageryCo-spaceDeep neural networkArticle