On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms

Theis, Fabian J.; García, Gonzalo A.

doi:10.1016/j.sigpro.2005.05.032

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…It is noteworthy that in such case, the independent component obtained by FastICA does not contain morphological information of the MUAP, whereas the key information derived here is the motor unit spike train. This is an important improvement over previous ICA-based approaches for surface EMG decomposition [31–35]. …”

Section: Discussionmentioning

confidence: 97%

“…For example, Nakamura et al applied FastICA to extract MUAP trains from electrode array EMG signals [31] [32]. Different ICA algorithms have been tested for separating the MUAP trains [33–35]. Unfortunately, previous ICA-based methods demonstrated limited success for surface EMG decomposition, because the primary effect of ICA processing appeared to be increasing the sparseness of the signal, rather than separating the activity from single motor units (see a recent review [39]).…”

Section: Discussionmentioning

confidence: 99%

“…These arrays make it feasible to extract single motor unit activity from surface EMG at very low force levels with two-dimensional template matching methods [28] [29]. More complicated multiple channel signal processing techniques were also proposed for high density surface EMG decomposition [30–35]. For example, among these efforts (with high density surface EMG) a most distinguished achievement was by Holobar and colleagues, who developed decomposition methods based on convolution kernel compensation (CKC) [26] [36–38], which allowed extraction of a number of simultaneously discharged motor units from high density surface EMG.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Chen

Zhou

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

187

147

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This study presents a progressive FastICA peel-off (PFP) framework for high density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a “peel off” strategy (i.e. removal of the estimated motor unit action potential (MUAP) trains from the previous step) is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. Moreover, a constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve the decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and signal to noise ratios (SNRs) (20, 10, 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was also tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous (FDI) muscle contraction at different intensities. On average 14.1 ± 5.0 motor units were identified from each trial of experimental surface EMG signals.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Chen

Zhou

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

187

147

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“…Subsequently, a variety of techniques, including pattern recognition and blind source separation (BSS), that are capable of handling the multi-channel surface EMG data have also been developed [12-14]. Kleine et al [15] investigated the importance of two-dimensional (2D) spatial filters in decomposing surface EMG signals, and the presented results demonstrate that the proposed 2D spatial filtering approach can detect the firing times of MUs with a high level of accuracy, but has difficulty in separating MUs with identical MUAP shapes.…”

Section: Introductionmentioning

confidence: 99%

Surface EMG Decomposition Based on <italic>K</italic>-means Clustering and Convolution Kernel Compensation

Ning

Zhu

et al. 2015

IEEE J. Biomed. Health Inform.

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A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multi-step iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering - Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of −10dB. Over 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A ‘two-source’ test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multi-channel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

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“…Because the component signal is similar to the particle signal in time and frequency, it is difficult to classify loose particle signals and component signals, especially when the component information provided by one channel is not comprehensive. However, the successful use of a multichannel analysis technique in electromyography (De et al, 2006;Theis and Garcia, 2006) and electroencephalography (Kus et al, 2004;Wang et al, 2010) signals overlapped with several noises supplies a potential application of multichannel recordings to discriminate loose particle signals and component signals.…”

Section: Introductionmentioning

confidence: 99%

Detecting loose particle signals in multichannel recordings with transductive confidence predictor

Zhai

Chen

2014

Transactions of the Institute of Measurement and Control

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Loose particles left inside sealed electronic devices are one of the main factors affecting the reliability of the entire military and aerospace system. The main purpose of loose particle detection is to discriminate between the particle signals and inherent mechanical noise (component signals). To address this problem, a multichannel system for loose particle detection is presented in this paper. A synchronization-based method is proposed, which makes good use of multichannel information. The synchronization method takes variance of correlation coefficients of multichannel synchronized pulses as a detection feature and then employs a transductive confidence predictor to produce confidence measures. The experimental results show the effectiveness of the proposed method.

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On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms

Cited by 21 publications

References 27 publications

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Surface EMG Decomposition Based on <italic>K</italic>-means Clustering and Convolution Kernel Compensation

Detecting loose particle signals in multichannel recordings with transductive confidence predictor

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