Real-Time Motor Unit Identification From High-Density Surface EMG

Glaser, Vojko; Holobar, Aleš; Zazula, Damjan

doi:10.1109/tnsre.2013.2247631

Cited by 111 publications

(101 citation statements)

References 34 publications

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“…where × = × × /(√ − 1) are referred to as the loadings 37 . Once the PCA and VARIMAX rotation are obtained in a training set (without any kinematic labeling), the control of the prosthesis test is achieved by inputing the motor neuron discharge time series in real time to the PCA subspace and VARIMAX projections, which can be combined to decrease the computational complexity.…”

Section: Experiments 3: Control Of Multiple Degrees Of Freedommentioning

confidence: 99%

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

et al. 2017

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The intuitive control of upper - limb prostheses requires a man/machine interface that directly exploits biological signals. Here, we define and experimentally test an offline man/machine interface that takes advantage of the discharge timings of spinal moto r neurons. The motor - neuron behaviour is identified by deconvolution of the electrical activity of muscles reinnervated by nerves of a missing limb in patients with amputation at the shoulder or humeral level. We mapped the series of motor - neuron discharge s into control commands across multiple degrees of freedom via the offline application of direct proportional control, pattern recognition and musculoskeletal modelling. A series of experiments performed on six patients reveal that the man/machine interfac e has superior offline performance than conventional direct electromyographic control applied after targeted muscle innervation. The combination of surgical procedures, decoding and mapping into effective commands constitutes an interface with the output l ayers of the spinal cord circuitry that allows for the intuitive control of multiple degrees of freedom

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Section: Experiments 3: Control Of Multiple Degrees Of Freedommentioning

confidence: 99%

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

et al. 2017

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“…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%

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

Chen

Zhou

2016

IEEE Trans. Neural Syst. Rehabil. Eng.

182

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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“…[11]). The work we present here is a novel method for estimating the number of MUs and their firing rates in surface EMGs.…”

Section: Discussionmentioning

confidence: 99%

A new cyclostationarity-based blind approach for motor unit's firing rate automated detection in electromyographic signals

Roussel

Haritopoulos

Ravier

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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This work focuses on electromyographic (EMG) signal processing. We propose a new blind approach that aims at detecting the firing rates of the activated motor units. The proposed method is based on the fact that, EMGs can be modelled as second-order cyclostationary signals. After application of a Blind Source Separation (BSS) algorithm, we compute a cyclostationarity measure which is the Cyclic Spectral Density (CSD), and we show how one can use it to group the estimated components into independent subspaces and in an automated manner. The proposed classification procedure is based on the concept of subspace BSS techniques, like the Multidimensional Independent Component Analysis (MICA), the difference being that our method allows automatic classification of the estimated source signals. After discarding the subspace corresponding to the noise and computation of a modified CSD measure, the proposed procedure yields to the detection of specific cyclic frequencies corresponding to the discharge frequencies of the Motor Units Action Potential Trains (MUAPTs). Early results obtained from experiments on synthetic EMGs are presented in the paper and research perspectives conclude this work.

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Real-Time Motor Unit Identification From High-Density Surface EMG

Cited by 111 publications

References 34 publications

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

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

A new cyclostationarity-based blind approach for motor unit's firing rate automated detection in electromyographic signals

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