Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques
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...
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| Format: | Article |
| Language: | English |
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Koya University
2023-10-01
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| Series: | ARO-The Scientific Journal of Koya University |
| Subjects: | |
| Online Access: | https://bp.koyauniversity.org/index.php/aro/article/view/1269 |
| _version_ | 1797643767018160128 |
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| author | Huda M. Radha Alia K. Abdul Hassan Ali H. Al-Timemy |
| author_facet | Huda M. Radha Alia K. Abdul Hassan Ali H. Al-Timemy |
| author_sort | Huda M. Radha |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-03-11T14:19:42Z |
| format | Article |
| id | doaj.art-d7b92da3de2d4e98bd3c1518a734b15e |
| institution | Directory Open Access Journal |
| issn | 2410-9355 2307-549X |
| language | English |
| last_indexed | 2024-03-11T14:19:42Z |
| publishDate | 2023-10-01 |
| publisher | Koya University |
| record_format | Article |
| series | ARO-The Scientific Journal of Koya University |
| spelling | doaj.art-d7b92da3de2d4e98bd3c1518a734b15e2023-10-31T19:16:46ZengKoya UniversityARO-The Scientific Journal of Koya University2410-93552307-549X2023-10-0111210.14500/aro.11269Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning TechniquesHuda M. Radha0Alia K. Abdul Hassan1Ali H. Al-Timemy2Department of Computer Science, College of Science, University of Baghdad, Baghdad, IraqDepartment of Computer Science, University of Technology, Baghdad, IraqDepartment of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq 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. https://bp.koyauniversity.org/index.php/aro/article/view/1269Upper limb amputeesProsthetic controlEEG and EMG signalsMachine learningMovement recognition |
| spellingShingle | Huda M. Radha Alia K. Abdul Hassan Ali H. Al-Timemy 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 Upper limb amputees Prosthetic control EEG and EMG signals Machine learning Movement recognition |
| title | Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques |
| title_full | Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques |
| title_fullStr | Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques |
| title_full_unstemmed | Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques |
| title_short | Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques |
| title_sort | enhancing upper limb prosthetic control in amputees using non invasive eeg and emg signals with machine learning techniques |
| topic | Upper limb amputees Prosthetic control EEG and EMG signals Machine learning Movement recognition |
| url | https://bp.koyauniversity.org/index.php/aro/article/view/1269 |
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