Electromyographic (<scp>EMG</scp>) Decomposition

Marateb, Hamid Reza; McGill, Kevin C.

doi:10.1002/047134608x.w8296

Cited by 7 publications

(16 citation statements)

References 115 publications

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“…26 However, recently, the use of EMG decomposition has been proposed for establishing neural interfaces in patients with limb amputations. 27 In these patients, nerve activity can be indirectly recorded by guiding nerves that previously innervated the missing limb muscles into accessary muscles that are used as biological amplifiers of nerve activity.…”

Section: 25mentioning

confidence: 99%

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A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

Karimimehr

Marateb

Muceli

et al. 2017

Int. J. Neur. Syst.

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The neural command from motor neurons to muscles -sometimes referred to as the neural drive to muscle -can be identified by decomposition of electromyographic (EMG) paper proposes for the first time an innovative method for fully automatic and real-time intramuscular EMG (iEMG) decomposition. The method is based on online single-pass density-based clustering and adaptive classification of bivariate features, using the concept of potential measure. No attempt was made to resolve superimposed motor unit action potentials. The proposed algorithm was validated on sets of simulated and experimental iEMG signals. Signals were recorded from the biceps femoris long-head, vastus medialis and lateralis and tibialis anterior muscles during low-to-moderate isometric constant-force and linearly-varying force contractions. The average number of missed, duplicated and erroneous clusters for the examined signals was 0.5 ± 0.8, 1.2 ± 1.0, and 1.0 ± 0.8, respectively. The average decomposition accuracy (defined similar to signal detection theory but without using True Negatives in the denominator) and coefficient of determination (variance accounted for) for the cumulative discharge rate estimation were 70 ± 9%, and 94 ± 5%, respectively. The time cost for processing each 200 ms iEMG interval was 43 ± 16 (21-97) ms. However, computational time generally increases over time as a function of frames/signal epochs. Meanwhile, the incremental accuracy defined as the accuracy of real-time analysis of each signal epoch, was 74 ± 18% for epochs recorded after initial one second. The proposed algorithm is thus a promising new tool for neural decoding in the next-generation of prosthetic control.

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Section: 25mentioning

confidence: 99%

“…Thus, most of the volume-conducted distant MUAPs are removed from the iEMG signal. 26 However, these MUAPs are generally small, and have similar shapes. Therefore, it is difficult to correctly classify such MUAPs.…”

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confidence: 99%

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

Karimimehr

Marateb

Muceli

et al. 2017

Int. J. Neur. Syst.

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“…EMG signals could be recorded either non-invasively (surface EMG, sEMG) or invasively with needle and wire electrodes (intramuscular EMG, iEMG) [ 11 ]. Although the iEMG has higher signal-to-noise ratio, both approaches provide a similar quality of identification of upper-arm motor task [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

Jordanic

Rojas-Martínez

Mañanas

et al. 2017

Sensors

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Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.

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“…In the case of motor system, this can be performed either at the level of motor nerves or motor units. 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).…”

Section: Discussionmentioning

confidence: 99%

“…Direct analysis of neural codes by decomposing the EMG (Webster et al, 2016), also known as neural decoding (Farina et al, 2014b; Karimimehr et al, 2017), is an alternative to EMG amplitude estimation (Gallego et al, 2017; Úbeda et al, 2017). It represents a paradigm shift, because it enables direct assessment of the neural drive to muscles (Farina and Holobar, 2015, 2016; Karimimehr et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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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Electromyographic (EMG) Decomposition

Cited by 7 publications

References 115 publications

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular EMG Recordings

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

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

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