Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition
Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with exten...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2016/3489540 |
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| author | Maoqi Chen Ales Holobar Xu Zhang Ping Zhou |
| author_facet | Maoqi Chen Ales Holobar Xu Zhang Ping Zhou |
| author_sort | Maoqi Chen |
| collection | DOAJ |
| description | Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6±4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85±1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield. |
| format | Article |
| id | doaj-art-45139b5e694f40bc855cd432e5318f7f |
| institution | Kabale University |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-45139b5e694f40bc855cd432e5318f7f2025-02-03T01:02:12ZengWileyNeural Plasticity2090-59041687-54432016-01-01201610.1155/2016/34895403489540Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG DecompositionMaoqi Chen0Ales Holobar1Xu Zhang2Ping Zhou3Biomedical Engineering Program, University of Science and Technology of China, Hefei, ChinaFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SloveniaBiomedical Engineering Program, University of Science and Technology of China, Hefei, ChinaGuangdong Work Injury Rehabilitation Center, Guangzhou, ChinaDecomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6±4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85±1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.http://dx.doi.org/10.1155/2016/3489540 |
| spellingShingle | Maoqi Chen Ales Holobar Xu Zhang Ping Zhou Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition Neural Plasticity |
| title | Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition |
| title_full | Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition |
| title_fullStr | Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition |
| title_full_unstemmed | Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition |
| title_short | Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition |
| title_sort | progressive fastica peel off and convolution kernel compensation demonstrate high agreement for high density surface emg decomposition |
| url | http://dx.doi.org/10.1155/2016/3489540 |
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