Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms

Šavc, Martin; Glaser, Vojko; Kranjec, Jernej; Cikajlo, Imre; Matjačić, Zlatko; Holobar, Aleš

doi:10.1109/tnsre.2018.2869426

Cited by 12 publications

(13 citation statements)

References 45 publications

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“…Although, the methods are very similar, the former is called “spike sorting” in computational neuroscience while the latter is known as “EMG decomposition,” or “decoding of the neural drive to the muscles” (Rey et al, 2015; Webster et al, 2016; Karimimehr et al, 2017). Such a decomposition algorithm could be used in variety of applications, including prosthesis control (Farina et al, 2014a) or robot-assisted neurorehabilitation (Savc et al, 2018). The structure of the spike sorting algorithms is, in principle, similar to that of iEMG decomposition methods, where the recording electrodes are close to the MU fibers.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, MU identification can be thought as a spike sorting algorithm (typical for computational neuroscience) applied to the outer layer of the human motor system (Balasubramanian and Obeid, 2011; Pani et al, 2016). The results of this MU identification can not only be used in EMG-EEG coupling analysis, but also in variety of research areas such as clinical neurophysiology for diagnosing neuromuscular disorders (Wheeler et al, 2006; Povalej BrŽan et al, 2017), sports and behavioral science (Merletti and Parker, 2004), movement science (Winter, 2009), robot-assisted rehabilitation (Savc et al, 2018), brain machine interface (Werner et al, 2016), and prosthesis control (Yoshida et al, 2010; Farina et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

“…Variety of sEMG decomposition methods have been introduced in the literature. Generally, they either use shape-based algorithms, also called template matching (Xu et al, 2001; Gazzoni et al, 2004; Garcia et al, 2005; De Luca et al, 2006; Ren et al, 2006; Kleine et al, 2007; Nawab et al, 2008; Winslow et al, 2009; Siqueira Júnior and Soares, 2015) or the blind source separation (BSS) algorithm (Holobar and Zazula, 2004, 2007; Glaser et al, 2013; Ning et al, 2013, 2015; Chen and Zhou, 2016; Negro et al, 2016; Savc et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Mohebian

Marateb

Karimimehr

et al. 2019

Front. Comput. Neurosci.

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Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Mohebian

Marateb

Karimimehr

et al. 2019

Front. Comput. Neurosci.

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“…The major challenge was how to reduce the amount of HDEMG data and present it as a real-time biofeedback. Usually a single information is provided as a feedback [3], but we found that fusion of all rows and 4 columns of electrodes can e ciently eliminate overestimation of muscle activation [18]. The RMS (root mean square) of the signals, recorded by electrodes in columns 1-4 presented the rst circle, columns 4-7 the second, 7-10 the third and the columns from 10-13 the last circle on the display (Fig.…”

Section: Emg Biofeedbackmentioning

confidence: 96%

Robotic Wrist Rehabilitation With High-density Electromyographic Biofeedback for Reducing Co-activation of Antagonist Muscles and Improved Motor Coordination: A Feasibility Study

Cikajlo¹,

Zadravec²,

Matjačić³

et al. 2020

Preprint

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Objectives: Electromygraphic (EMG) biofeedback has been successfully applied in stroke survivors for a few decades. Recently high-density (HD) electrodes were introduced in research and diagnostic EMG. The application of such electrodes may increase the reliability and selectivity, but requires information reduction for real-time biofeedback. We tested whether the use of the proposed feedback is feasible in minimization of inappropriate co-activation of wrist flexors/extensors controlled by the wrist rehabilitation robot. Methods: The EMG of wrist flexors and extensors muscles has been assessed by arrays of 5 x 13 electrodes, reduced to 1 x 4 information matrix and provided as real-time biofeedback during wrist movement controlled by the robot. The designed task required to keep the contribution of agonist muscles within predefined limits. A feasibility study with a single patient with stroke was conducted to examine force tracking and reduction of activity of antagonists wrist muscles in 4 weeks. Results: Force tracking signal and cumulative forecast errors decreased in 4 trials. The RMS activity of antagonist muscles significantly decreased (Kruskal Wallis, c2 = 3.330*10-9) during wrist flexion in the 4th week of trials. The participant was able to follow the reduced HDEMG biofeedback.Conclusions: The outcomes show that the surface HDEMG biofeedback has a potential to decrease the co-activation of antagonist muscles, wrist flexion in particular. Additionally the combination of force tracking and robot assisted biofeedback training may recover selective hand function in stroke survivors who would less likely to develop contractures. Trial registration: The study is not a clinical trial. While human subjects are involved, they do not participate in a full rehabilitation intervention, and no health outcomes are examined.

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“…Finally, to see how good our proposed technique is, we also perform a comparison with another feature extraction algorithm which are principal component analysis (PCA) [17], non-negative matrix factorization (NMF) [18], singular value decomposition (SVD) [19] [20], and with raw data processing. The process diagram of this experiment is shown in Figure 3.…”

Section: Performance Measurement and Comparisonmentioning

confidence: 99%

A New Feature Extraction Algorithm to Extract Differentiate Information and Improve KNN-based Model Accuracy on Aquaculture Dataset

Natan

Gunawan

Dewantara

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the world of aquaculture, understanding the condition of a pond is very important for a farmer in deciding which action should they take to prevent any bad condition occurred. Condition of a pond can be justified by measuring plenty of water parameters which can be divided into 3 categories that are physical, chemical and biological. The physical parameter is any physical quantity that can be measured in the pond. The chemical parameter is any kind of chemical substances that are dissolved in water. The biological parameter is any organic matter that lives in water. However, all of these parameters are not so distinguishable in representing the condition of a pond. Therefore, the farmer experience difficulties in justifying the condition and taking proper action to their pond. Even with the help of the K-Nearest Neighbors (KNN) algorithm combined with grid search optimization to model the data, the result is still not satisfying where the model only achieve accuracy of 0.701 in leave one out validation. To overcome this problem, a kind of feature extraction algorithm is needed to extract more information and make the data become more differentiate in representing the condition of the pond. With the help of our proposed feature extraction algorithm, optimized KNN can model the data easier and achieve higher accuracy. From the experiment results, the proposed feature extraction algorithm gives an impressive performance where it increases the accuracy to 0.741. A comparison with other feature extraction algorithms such as Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Singular Value Decomposition (SVD) is also conducted to validate how good the proposed feature extraction algorithm is. As a result, the proposed algorithm is surpassing the other algorithms which only achieve the accuracy of 0.707, 0.718, and 0.718, respectively.

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Comparison of Convolutive Kernel Compensation and Non-Negative Matrix Factorization of Surface Electromyograms

Cited by 12 publications

References 45 publications

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Robotic Wrist Rehabilitation With High-density Electromyographic Biofeedback for Reducing Co-activation of Antagonist Muscles and Improved Motor Coordination: A Feasibility Study

A New Feature Extraction Algorithm to Extract Differentiate Information and Improve KNN-based Model Accuracy on Aquaculture Dataset

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