Analysis And Classification Of Motor Imagery Using Deep Neural Network
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Date
2021-05-25
Journal Title
Journal ISSN
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Publisher
institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia
Abstract
Motor 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.
Description
Keywords
Brain-computer interface, Electroencephalography, Motor imagery, Co-space, Deep neural network