Analysis and Classification of Motor Imagery Using Deep Neural Network - Isah Salim Ahmad; Shuai Zhang; Sani Saminu; Isselmou Abd El Kader; Jamil maaruf musa; Imran Javid; Souha Kamhi; Ummay Kulsum (AMTS and Faculty of Engineering - Universitas Riau, 2021)
Motor imagery based on brain-computer interface (BCI) has attracted important research attention 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 left 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 classification of left and right movement of EEG signal using Common Spatial Pattern (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 classification accuracy is 87%, which is improved as compared with state-of-the-art methods that used different datasets.