Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems
Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational comp...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1506042/full |
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| author | Maham Nayab Asim Waris Muhammad Jawad Khan Dokhyl AlQahtani Ahmed Imran Syed Omer Gilani Umer Hameed Shah |
| author_facet | Maham Nayab Asim Waris Muhammad Jawad Khan Dokhyl AlQahtani Ahmed Imran Syed Omer Gilani Umer Hameed Shah |
| author_sort | Maham Nayab |
| collection | DOAJ |
| description | Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. This approach aims to improve the classification accuracy of EMG signals while substantially reducing computational costs, offering valuable implications for all EMG-related processes on such data. The proposed methodology involves extracting time and frequency domain features from twelve channels of EMG signals, followed by dimensionality reduction using techniques such as PCA, LDA, PPCA, Lasso and GPLVM, and classification using an ANN. Our investigation revealed that LDA is not appropriate for this dataset. The dimensionality reduction models did not have any significant effect on the accuracy, but the computational cost decreased significantly. In individual comparisons, GPLVM had the shortest computational time (29 s), which was significantly less than that of all the other models (p < 0.05), with PCA following at approximately 35 s and Relief at approximately 57 s, while PPCA took approximately 69 s, and Lasso exhibited higher computational costs than all the models but lower computational costs than did the original set. Using the best-performing features, all possible sets of 2, 3, 4 and 5 features were tested, and the 5-feature set exhibited the best performance. This research demonstrates the effectiveness of dimensionality reduction and feature selection in improving the accuracy of movement recognition in myoelectric control. |
| format | Article |
| id | doaj-art-e8581375e7a74ac2a5de5580fa61d590 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-e8581375e7a74ac2a5de5580fa61d5902025-01-22T07:11:27ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01810.3389/frai.2025.15060421506042Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systemsMaham Nayab0Asim Waris1Muhammad Jawad Khan2Dokhyl AlQahtani3Ahmed Imran4Syed Omer Gilani5Umer Hameed Shah6National University of Science and Technology, Islamabad, PakistanNational University of Science and Technology, Islamabad, PakistanDepartment of Electrical Engineering, School of Engineering, Prince Sattam Bin Abdul Aziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical Engineering, School of Engineering, Prince Sattam Bin Abdul Aziz University, Al-Kharj, Saudi ArabiaDepartment of Biomedical Engineering and Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesAbu Dhabi University, Abu Dhabi, United Arab EmiratesDepartment of Mechanical Engineering and Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesElectromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. This approach aims to improve the classification accuracy of EMG signals while substantially reducing computational costs, offering valuable implications for all EMG-related processes on such data. The proposed methodology involves extracting time and frequency domain features from twelve channels of EMG signals, followed by dimensionality reduction using techniques such as PCA, LDA, PPCA, Lasso and GPLVM, and classification using an ANN. Our investigation revealed that LDA is not appropriate for this dataset. The dimensionality reduction models did not have any significant effect on the accuracy, but the computational cost decreased significantly. In individual comparisons, GPLVM had the shortest computational time (29 s), which was significantly less than that of all the other models (p < 0.05), with PCA following at approximately 35 s and Relief at approximately 57 s, while PPCA took approximately 69 s, and Lasso exhibited higher computational costs than all the models but lower computational costs than did the original set. Using the best-performing features, all possible sets of 2, 3, 4 and 5 features were tested, and the 5-feature set exhibited the best performance. This research demonstrates the effectiveness of dimensionality reduction and feature selection in improving the accuracy of movement recognition in myoelectric control.https://www.frontiersin.org/articles/10.3389/frai.2025.1506042/fullANNdimensionality reductionfeature selectionGPLVMmyoelectric controlPCA |
| spellingShingle | Maham Nayab Asim Waris Muhammad Jawad Khan Dokhyl AlQahtani Ahmed Imran Syed Omer Gilani Umer Hameed Shah Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems Frontiers in Artificial Intelligence ANN dimensionality reduction feature selection GPLVM myoelectric control PCA |
| title | Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems |
| title_full | Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems |
| title_fullStr | Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems |
| title_full_unstemmed | Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems |
| title_short | Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems |
| title_sort | gaussian process latent variable models ann based method for automatic features selection and dimensionality reduction for control of emg driven systems |
| topic | ANN dimensionality reduction feature selection GPLVM myoelectric control PCA |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1506042/full |
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