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All Journal Journal of Applied Materials and Technology
Isah Salim Ahmad
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology Tianjin 300130, China
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering Mechanical Engineering

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Journal : Journal of Applied Materials and Technology

 1 
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
Journal of Applied Materials and Technology Vol. 2 No. 2 (2021): March 2021
Publisher : AMTS and Faculty of Engineering - Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/Jamt.2.2.85-93

Abstract

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.
Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition Sani Saminu; Guizhi Xu; Zhang Shuai; Abd El Kader Isselmou; Adamu Halilu Jabire; Ibrahim Abdullahi Karaye; Isah Salim Ahmad; Abubakar Abdulkarim
Journal of Applied Materials and Technology Vol. 2 No. 2 (2021): March 2021
Publisher : AMTS and Faculty of Engineering - Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/Jamt.2.2.74-84

Abstract

The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).
Co-Authors Abd El Kader Isselmou Abubakar Abdulkarim Adamu Halilu Jabire Guizhi Xu Ibrahim Abdullahi Karaye Imran Javid Isselmou Abd El Kader Jamil maaruf musa Sani Saminu Sani Saminu Shuai Zhang Souha Kamhi Ummay Kulsum Zhang Shuai