Experimental Analysis of Accuracy in the Identification of Motor Unit Spike Trains From High-Density Surface EMG

Holobar, Aleš; Minetto, Marco Alessandro; Botter, Alberto; Negro, Francesco; Farina, Dario

doi:10.1109/tnsre.2010.2041593

Cited by 217 publications

(242 citation statements)

References 21 publications

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“…The accuracy measure, Acc, used in our study was similar to that proposed by Holobar et al and Negro et al as the rate of agreement between two decomposition results. 24,55 Other similar accuracy measures have been used in the literature.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

“…The complexity of each EMG signal was characterized in terms of the signal-to-interference ratio (SIR), 55 which is an estimate of the percentage of the signal energy explained by the manual decomposition results. The MDR and coefficient of inter-spike interval (ISI) variation (CoV) of each MUAP train were estimated using the algorithm described in Ref.…”

mentioning

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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“…However, recent attempts to harness the full spectrum of EMG signals available have shown that high density (HD) EMG can provide the full discharge patterns of active motor units [74][75][76][77], which may be a future alternative source of control.…”

Section: Surgical Methods To Improve Controlmentioning

confidence: 99%

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

et al. 2014

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Control algorithms for upper limb myoelectric prostheses have been in development since the mid-1940s. Despite advances in computing power and in the performance of these algorithms, clinically available prostheses are still based on the earliest control strategies. The aim of this review paper is to detail the development, advantages and disadvantages of prosthetic control systems and to highlight areas that are current barriers for the transition from laboratory to clinical practice. Current surgical strategies and future research directions to achieve multifunctional control will also be discussed. The findings from this review suggest that regression algorithms may offer an alternative feed-forward approach to direct and pattern recognition control, while virtual rehabilitation environments and tactile feedback could improve the overall prosthetic control.

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“…The convolution kernel compensation method, introduced in Zazula (2004, 2007) and validated in numerous previous studies Holobar et al 2009Holobar et al , 2010Holobar et al , 2012Marateb et al 2011), was used to decompose the acquired EMG signals into contributions of individual motor units. Once identified, discharge times of individual motor units were dynamically tracked over the entire EMG signal, taking into account potential changes in shapes of motor unit action potentials (MUAPs), such as those caused by small arm movements and fatigue (Holobar et al , 2010(Holobar et al , 2012.…”

Section: Subjectsmentioning

confidence: 99%

Preferential distribution of nociceptive input to motoneurons with muscle units in the cranial portion of the upper trapezius muscle

Dideriksen

Holobar

Falla

2016

Journal of Neurophysiology

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Dideriksen JL, Holobar A, Falla D. Preferential distribution of nociceptive input to motoneurons with muscle units in the cranial portion of the upper trapezius muscle. J Neurophysiol 116: 611-618, 2016. First published May 25, 2016 doi:10.1152/jn.01117.2015Pain is associated with changes in the neural drive to muscles. For the upper trapezius muscle, surface electromyography (EMG) recordings have indicated that acute noxious stimulation in either the cranial or the caudal region of the muscle leads to a relative decrease in muscle activity in the cranial region. It is, however, not known if this adaption reflects different recruitment thresholds of the upper trapezius motor units in the cranial and caudal region or a nonuniform nociceptive input to the motor units of both regions. This study investigated these potential mechanisms by direct motor unit identification. Motor unit activity was investigated with high-density surface EMG signals recorded from the upper trapezius muscle of 12 healthy volunteers during baseline, control (intramuscular injection of isotonic saline), and painful (hypertonic saline) conditions. The EMG was decomposed into individual motor unit spike trains. Motor unit discharge rates decreased significantly from control to pain conditions by 4.0 Ϯ 3.6 pulses/s (pps) in the cranial region but not in the caudal region (1.4 Ϯ 2.8 pps; not significant). These changes were compatible with variations in the synaptic input to the motoneurons of the two regions. These adjustments were observed, irrespective of the location of noxious stimulation. These results strongly indicate that the nociceptive synaptic input is distributed in a nonuniform way across regions of the upper trapezius muscle.

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Experimental Analysis of Accuracy in the Identification of Motor Unit Spike Trains From High-Density Surface EMG

Cited by 217 publications

References 21 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

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

Preferential distribution of nociceptive input to motoneurons with muscle units in the cranial portion of the upper trapezius muscle

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