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

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Maoqi Chen ◽  
Ales Holobar ◽  
Xu Zhang ◽  
Ping Zhou
Keyword(s):  
Motor Units ◽  
Surface Emg ◽  
High Density ◽  
Convolution Kernel ◽  
High Agreement ◽  
Density Surface ◽  
Processing Techniques ◽  
High Degree ◽  
Neurologically Intact ◽  
Signal Processing Techniques

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±4.3common motor units were identified from each trial, which showed a very high matching rate of97.85±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.

Excitability tests using high-density surface-EMG: A novel approach to studying single motor units

2018 ◽  
Vol 129 (8) ◽  
pp. 1634-1641 ◽  
Author(s):  
Boudewijn T.H.M. Sleutjes ◽  
Judith Drenthen ◽  
Ernest Boskovic ◽  
Leonard J. van Schelven ◽  
Maria O. Kovalchuk ◽  
...  
Keyword(s):  
Motor Units ◽  
Surface Emg ◽  
High Density ◽  
Single Motor ◽  
Single Motor Units ◽  
Novel Approach ◽  
Density Surface

Topographical Characteristics of Motor Units of the Lower Facial Musculature Revealed by Means of High-Density Surface EMG

2006 ◽  
Vol 95 (1) ◽  
pp. 342-354 ◽  
Author(s):  
Bernd G. Lapatki ◽  
Robert Oostenveld ◽  
Johannes P. Van Dijk ◽  
Irmtrud E. Jonas ◽  
Machiel J. Zwarts ◽  
...  
Keyword(s):  
Motor Units ◽  
Surface Emg ◽  
High Density ◽  
Facial Musculature ◽  
Density Surface

Analysis of High-Density Surface EMG and Finger Pressure in the Left Forearm of Violin Players: A Feasibility Study

2017 ◽  
Vol 32 (3) ◽  
pp. 139-151 ◽  
Author(s):  
Paolo Cattarello ◽  
Roberto Merletti ◽  
Francesco Petracca
Keyword(s):  
Pressure Sensors ◽  
Surface Emg ◽  
High Density ◽  
String Instruments ◽  
Activation Patterns ◽  
Study Objective ◽  
Left Hand ◽  
Density Surface ◽  
The Right ◽  
Finger Pressure

Wrist and finger flexor muscles of the left hand were evaluated using high-density surface EMG (HDsEMG) in 17 violin players. Pressure sensors also were mounted below the second string of the violin to evaluate, simultaneously, finger pressure. Electrode grid size was 110x70 mm (12x8 electrodes with interelectrode distance=10 mm and Ø=3 mm). The study objective was to observe the activation patterns of these muscles while the violinists sequentially played four notes—-SI (B), DO# (C#), RE (D), MI (E)—-at 2 bows/s (one bow up in 0.5 s and one down in 0.5 s) and 4 bows/s on the second string, while producing a constant (CONST) or ramp (RAMP) sound volume. HDsEMG images obtained while playing the notes were compared with those obtained during isometric radial or ulnar flexion of the wrist or fingers. Two image descriptors provided information on image differences. Results showed that the technique was reliable and provided reliable signals, and that recognizably different sEMG images could be associated with the four notes tested, despite the variability within and between subjects playing the same note. sEMG activity of the left hand muscles and pressure on the string in the RAMP task were strongly affected in some individuals by the sound volume (controlled by the right hand) and much less in other individuals. These findings question whether there is an individual or generally optimal way of pressing violin strings with the left hand. The answer to this question might substantially modify the teaching of string instruments.

Identifying Motor Units in Longitudinal Studies with High-Density Surface Electromyography

2016 ◽  
pp. 147-151
Author(s):  
Eduardo Martinez-Valdes ◽  
Francesco Negro ◽  
Christopher M. Laine ◽  
Deborah L. Falla ◽  
Frank Mayer ◽  
...  
Keyword(s):  
Longitudinal Studies ◽  
Surface Electromyography ◽  
Motor Units ◽  
High Density ◽  
Density Surface

scholarly journals Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings

2015 ◽  
Vol 25 (06) ◽  
pp. 1550024 ◽  
Author(s):  
Yang Liu ◽  
Yong Ning ◽  
Sheng Li ◽  
Ping Zhou ◽  
William Z. Rymer ◽  
...  
Keyword(s):  
High Performance ◽  
Unmet Need ◽  
Three Dimensional ◽  
Motor Units ◽  
Decomposition Methods ◽  
Surface Emg ◽  
Source Imaging ◽  
Dynamic Propagation ◽  
Channel Surface ◽  
Density Surface

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

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