Open AccessAnalysis and Classification of Motor Imagery Using Deep Neural Network
Author(s) -
Isah Salim Ahmad,
Shuai Zhang,
Sani Saminu,
Isselmou Abd El Kader,
Jamil maaruf musa,
Imran Javid,
Souha Kamhi,
Ummay Kulsum
Publication year - 2021
Publication title -
journal of applied materials and technology
Language(s) - English
Resource type - Journals
eISSN - 2721-446X
pISSN - 2686-0961
DOI - 10.31258/jamt.2.2.85-93
Subject(s) - motor imagery , brain–computer interface , artificial intelligence , computer science , electroencephalography , confusion matrix , artificial neural network , deep learning , confusion , feature extraction , pattern recognition (psychology) , psychology , neuroscience , psychoanalysis
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.