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

Radha, Huda M. and Abdul Hassan, Alia K. and Al-Timemy, Ali H. (2023) Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 11 (2). pp. 99-108. ISSN 2410-9355

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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.

Item Type:	Article
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Weakening the gain-loss-ratio measure to make it stronger. Finance Research Letters, 12, p.58-66. DOI: https://doi.org/10.1016/j.frl.2014.11.007 VIEW THE PDF FILE PUBLISHED 2023-10-30 HOW TO CITE Radha, H. M., Abdul Hassan, A. K. and Al-Timemy, A. H. (2023) “Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 11(2), pp. 99-108. doi: 10.14500/aro.11269. More Citation Formats ISSUE Vol. 11 No. 2 (2023): Issue Twenty One SECTION Articles Copyright (c) 2023 Huda M. Radha, Alia K. Abdul Hassan, Ali H. Al-Timemy Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Authors who choose to publish their work with Aro agree to the following terms: Authors retain the copyright to their work and grant the journal the right of first publication. 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Uncontrolled Keywords:	Upper limb amputees, Prosthetic control, EEG and EMG signals, Machine learning, Movement recognition
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	ARO-The Scientific Journal of Koya University > VOL 11, NO 2 (2023)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	19 Dec 2023 09:36
Last Modified:	19 Dec 2023 09:36
URI:	http://eprints.koyauniversity.org/id/eprint/437

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