Analysis And Classification Of Motor Imagery Using Deep Neural Network

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dc.contributor.authorAhmad, Isah Salim
dc.contributor.authorZhang, Shuai
dc.contributor.authorSaminu, Sani
dc.contributor.authorIsselmou
dc.contributor.authorMusa, Jamilu Maaruf
dc.contributor.authorJavaid, Imran
dc.contributor.authorKAMHI, SOUHA
dc.contributor.authorKULSUM, UMMAY
dc.date.accessioned2022-01-10T11:29:37Z
dc.date.available2022-01-10T11:29:37Z
dc.date.issued2021-05-25
dc.description.abstractMotor 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.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7308
dc.language.isoenen_US
dc.publisherinstitution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesiaen_US
dc.subjectBrain-computer interfaceen_US
dc.subjectElectroencephalographyen_US
dc.subjectMotor imageryen_US
dc.subjectCo-spaceen_US
dc.subjectDeep neural networken_US
dc.titleAnalysis And Classification Of Motor Imagery Using Deep Neural Networken_US
dc.typeArticleen_US

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