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Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

An example of discharge instants for motor units identified from an isometric contraction at ~18% MVC. The red spike trains represent the results obtained by the CKC, and the blue ones represent the results obtained by the PFP. Black dots represent the locations where the two methods generated inconsistent discharge instants.
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fig1: An example of discharge instants for motor units identified from an isometric contraction at ~18% MVC. The red spike trains represent the results obtained by the CKC, and the blue ones represent the results obtained by the PFP. Black dots represent the locations where the two methods generated inconsistent discharge instants.

Mentions: Figure 1 shows an example of discharge instants for motor units identified from an isometric contraction at the level of approximately 18% MVC. The red spike trains represent the results obtained by the CKC, and the blue ones are the results obtained by the PFP. In this example, 19 common motor units were identified, whose discharge patterns are aligned together in the figure. Black dots represent few locations where the two methods generated inconsistent discharge instants. In addition, each method also identified two different motor units, respectively, as shown in the figure.


Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition
An example of discharge instants for motor units identified from an isometric contraction at ~18% MVC. The red spike trains represent the results obtained by the CKC, and the blue ones represent the results obtained by the PFP. Black dots represent the locations where the two methods generated inconsistent discharge instants.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC5015010&req=5

fig1: An example of discharge instants for motor units identified from an isometric contraction at ~18% MVC. The red spike trains represent the results obtained by the CKC, and the blue ones represent the results obtained by the PFP. Black dots represent the locations where the two methods generated inconsistent discharge instants.
Mentions: Figure 1 shows an example of discharge instants for motor units identified from an isometric contraction at the level of approximately 18% MVC. The red spike trains represent the results obtained by the CKC, and the blue ones are the results obtained by the PFP. In this example, 19 common motor units were identified, whose discharge patterns are aligned together in the figure. Black dots represent few locations where the two methods generated inconsistent discharge instants. In addition, each method also identified two different motor units, respectively, as shown in the figure.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus