Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques

Huda M. Radha; Alia K. Abdul Hassan; Ali H. Al-Timemy

doi:10.14500/aro.11269

Huda M. Radha Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0002-8041-7679
Alia K. Abdul Hassan Department of Computer Science, University of Technology, Baghdad, Iraq https://orcid.org/0000-0002-6835-8872
Ali H. Al-Timemy Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-2738-8896

Keywords: Upper limb amputees, Prosthetic control, EEG and EMG signals, Machine learning, Movement recognition

Abstract

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducted in this study utilized the Binary Grey Wolf Optimization (BGWO) algorithm to select optimal features for the proposed classification model. The results demonstrate promising outcomes, with an average classification accuracy of 93.6% for three amputees and five individuals with intact limbs. The accuracy achieved in classifying the seven types of hand and wrist movements further validates the effectiveness of the proposed approach. By offering a non-invasive and reliable means of recognizing upper limb movements, this research represents a significant step forward in biotechnical engineering for upper limb amputees. The findings hold considerable potential for enhancing the control and usability of prosthetic devices, ultimately contributing to the overall quality of life for individuals with upper limb amputations.

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Author Biographies

Huda M. Radha, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

Huda M. Ridha is a Lecturer at the Department of Computer Science, College of Science, University of Baghdad. She got a B.Sc. and M.Sc. degrees in Computer Science. Her research interests are in Artificial Intelligent, Image Processing, Data Mining and Bio-signals analysis.

Alia K. Abdul Hassan, Department of Computer Science, University of Technology, Baghdad, Iraq

Alia K. Abdul Hassan is a Professor at the Department of Computer Science, University of Technology, Baghdad, Iraq. She got her B.Sc. degree in Computer Science, the M.Sc. degree in Computer Science and the Ph.D. degree in Computer Science. Her research interests are in Artificial Intelligent, Soft computing, Data Mining, Software engineering, Electronic Management, and Computer security.

Ali H. Al-Timemy, Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq

Ali H. Al-Timemy is a Assistant Prof. at the Department of Biomedical Engineering, College of Al-Khwarizmi of Engineering, University of Baghdad, Iraq. He got his B.Sc. degree in Biomedical Engineering, the M.Sc. degree in Biomedical Engineering and Ph.D. degree in Biomedical Engineering] from the Centre for Robotics and Neural Systems (CRNS), Plymouth University, UK. . is research interests are in Bio-medical signal, Image processing, Ppattern recognition, Artificial intelligence and Machine Learning.

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Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques

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