Compressed spike-triggered averaging in iterative decomposition of surface EMG

Lundsberg, Jonathan; Björkman, Anders; Malešević, Nebojša; Antfolk, Christian

doi:10.1016/j.cmpb.2022.107250

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…(18) (19) Then the extended convolutional model can be expressed by the following matrix multiplication form: (20) where is a matrix containing all the waveform coefficients in a proper order (for details, please refer to [1]). Note that the equation ( 20) is equivalent to equation (17) when the delay factor is zero.…”

Section: D． 2cfastica For High Density Surface Emg Decomposition Now ...mentioning

confidence: 99%

“…For example, a decomposition framework combining FastICA and CKC was proposed right after the PFP [6]. A range of other forms of high density surface EMG decomposition methods have also been reported, with either CKC or FastICA as key elements [7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The peel-off strategy has proved to be very effective for overcoming the local convergence of FastICA. This strategy has been followed by recent studies in order to achieve higher decomposition yield [9,11,19].…”

Section: Introductionmentioning

confidence: 99%

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2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA

Chen,

Zhou

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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This study presents a novel high density surface electromyography (EMG) decomposition method, named as 2CFastICA, because it incorporates two key algorithms: kernel constrained FastICA and correlation constrained FastICA. The former focuses on overcoming the local convergence of FastICA without requiring the peel-off strategy used in the progressive FastICA peel-off (PFP) framework. The latter further refines the output of kernel constrained FastICA by correcting possible erroneous or missed spikes. The two constrained FastICA algorithms supplement each other to warrant the decomposition performance. The 2CFastICA method was validated using simulated surface EMG signals with different motor unit numbers and signal to noise ratios (SNRs). Two source validation was also performed by simultaneous high density surface EMG and intramuscular EMG recordings, showing a matching rate (MR) of (97.2 ± 3.5) % for 170 common motor units. In addition, a different form of two source validation was also conducted taking advantages of the high density surface EMG characteristics of patients with amyotrophic lateral sclerosis, showing a MR of (99.4 ± 0.9) % for 34 common motor units from interference and sparse datasets. Both simulation and experimental results indicate that 2CFastICA can achieve similar decomposition performance to PFP. However, the efficiency of decomposition can be greatly improved by 2CFastICA since the complex signal processing procedures associated with the peel-off strategy are not required any more. Along with this paper, we also provide the MATLAB open source code of 2CFastICA for high density surface EMG decomposition.

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Section: D． 2cfastica For High Density Surface Emg Decomposition Now ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA

Chen,

Zhou

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts

Zhao,

Zhang,

Chen

et al. 2024

Computer Methods and Programs in Biomedicine

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Inferring position of motor units from high-density surface EMG

Lundsberg,

Björkman,

Malesevic

et al. 2024

Sci Rep

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The spatial distribution of muscle fibre activity is of interest in guiding therapy and assessing recovery of motor function following injuries of the peripheral or central nervous system. This paper presents a new method for stable estimation of motor unit territory centres from high-density surface electromyography (HDsEMG). This completely automatic process applies principal component compression and a rotatable Gaussian surface fit to motor unit action potential (MUAP) distributions to map the spatial distribution of motor unit activity. Each estimated position corresponds to the signal centre of the motor unit territory. Two subjects were used to test the method on forearm muscles, using two different approaches. With the first dataset, motor units were identified by decomposition of intramuscular EMG and the centre position of each motor unit territory was estimated from synchronized HDsEMG data. These positions were compared to the positions of the intramuscular fine wire electrodes with depth measured using ultrasound. With the second dataset, decomposition and motor unit localization was done directly on HDsEMG data, during specific muscle contractions. From the first dataset, the mean estimated depth of the motor unit centres were 8.7, 11.6, and 9.1 mm, with standard deviations 0.5, 0.1, and 1.3 mm, and the respective depths of the fine wire electrodes were 8.4, 15.8, and 9.1 mm. The second dataset generated distinct spatial distributions of motor unit activity which were used to identify the regions of different muscles of the forearm, in a 3-dimensional and projected 2-dimensional view. In conclusion, a method is presented which estimates motor unit centre positions from HDsEMG. The study demonstrates the shifting spatial distribution of muscle fibre activity between different efforts, which could be used to assess individual muscles on a motor unit level.

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Compressed spike-triggered averaging in iterative decomposition of surface EMG

Cited by 4 publications

References 36 publications

2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA

2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA

A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts

Inferring position of motor units from high-density surface EMG

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