Optimal spatio-temporal filter for the reduction of crosstalk in surface electromyogram

Mesin, Luca

doi:10.1088/1741-2552/aa8f03

Cited by 20 publications

(16 citation statements)

References 33 publications

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“…Further studies should consider the influence of stroke lesion on the NMES modulatory effects, diffusion MRI and other neuroimaging techniques might be necessary to explore the cortico-spinal tract and investigate potential modulatory effects on the pyramidal tracts and motor cortex [80]. The complex crosstalk in the four EMG measurement channels made it challenging to remove the 40 Hz NMES artifact in EMG signals [81], the frequency band above 35 Hz was not considered. Future study should remove all the stimulation artifacts and perform a systematic analysis of the EMG signals during various pedaling.…”

Section: Discussionmentioning

confidence: 99%

Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors

Bao

Leung

Zhou

et al. 2019

J NeuroEngineering Rehabil

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BackgroundNeuromuscular electrical stimulation (NMES) is extensively used in stroke motor rehabilitation. How it promotes motor recovery remains only partially understood. NMES could change muscular properties, produce altered sensory inputs, and modulate fluctuations of cortical activities; but the potential contribution from cortico-muscular couplings during NMES synchronized with dynamic movement has rarely been discussed.MethodWe investigated cortico-muscular interactions during passive, active, and NMES rhythmic pedaling in healthy subjects and chronic stroke survivors. EEG (128 channels), EMG (4 unilateral lower limb muscles) and movement parameters were measured during 3 sessions of constant-speed pedaling. Sensory-level NMES (20 mA) was applied to the muscles, and cyclic stimulation patterns were synchronized with the EMG during pedaling cycles. Adaptive mixture independent component analysis was utilized to determine the movement-related electro-cortical sources and the source dipole clusters. A directed cortico-muscular coupling analysis was conducted between representative source clusters and the EMGs using generalized partial directed coherence (GPDC). The bidirectional GPDC was compared across muscles and pedaling sessions for post-stroke and healthy subjects.ResultsDirected cortico-muscular coupling of NMES cycling was more similar to that of active pedaling than to that of passive pedaling for the tested muscles. For healthy subjects, sensory-level NMES could modulate GPDC of both ascending and descending pathways. Whereas for stroke survivors, NMES could modulate GPDC of only the ascending pathways.ConclusionsBy clarifying how NMES influences neuromuscular control during pedaling in healthy and post-stroke subjects, our results indicate the potential limitation of sensory-level NMES in promoting sensorimotor recovery in chronic stroke survivors.

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Section: Discussionmentioning

confidence: 99%

Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors

Bao

Leung

Zhou

et al. 2019

J NeuroEngineering Rehabil

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“…For the comparison, three methods (FastICA [4], joint diagonalization of covariance matrices [5] and optimal filtering [6]) have been selected according to the specificity of the sEMG. Indeed, a classical modeling of the sEMG is a linear instantaneous mixture of the activation potentials firstname.lastname@gipsa-lab.grenoble-inp.fr generate by each muscle fiber, leading to…”

Section: Methodsmentioning

confidence: 99%

“…Optimal filtering [6] is a semi-blind source separation method in the sense that it requires a reference signal corresponding to the recording of the sEMG when only the target muscle is in contraction in addition to the recordings when several muscles are involved simultaneously. Let x 1 (t) denote the sEMG when only the target muscle is in contraction and x 2 (t) the sEMG when several muscles are in contraction simultaneously.…”

Section: Optimal Filteringmentioning

confidence: 99%

Comparison of Blind Source Separation Methods to Surface Electromyogram for Extensor Muscles of the Index and Little Fingers

Magbonde

Quaine

Rivet

2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Crosstalk is the result of the propagation of muscle electrical signals on surface electromyogram channels simultaneously. The objective of this paper is to study the behavior of three blind source separation (BSS) methods for crosstalk reduction during finger extensor muscle contractions: FastICA, joint diagonalization of covariance matrices and optimal filtering. These methods have been tested on artificial mixtures defined by a temporal sum of the real signals from isolated contraction of two independent biomechanical muscles for the extension of the index and little finger. Artificial mixtures display a ground truth for comparison between the methods. The separation was better using the optimal filtering compared to the other two methods. The optimal filtering have then be tested on real mixtures recorded during a simultaneous contraction of the two muscles. The results are less satisfactory but open doors to new perspectives.

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“…Despite these advantages of MMG, crosstalk (CT) is a drawback that hinders the use of MMG in clinical applications 11 , the development of prosthetic control 12,13 , observations of muscle function 14–17 , the monitoring of the motor unit activity 18–20 and observations on neuromuscular blockade 21 . CT can be defined as the contamination of the target muscle signals by the signals from muscles adjacent to the target muscle 22 .…”

Section: Introductionmentioning

confidence: 99%

Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks

Talib

Sundaraj

Lam

2019

Sci Rep

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This study aimed to quantify the association of four anthropometric parameters of the human arm, namely, the arm circumference (CA), arm length (LA), skinfold thickness (ST) and inter-sensor distance (ISD), with amplitude (RMS) and crosstalk (CT) of mechanomyography (MMG) signals. Twenty-five young, healthy, male participants were recruited to perform forearm flexion, pronation and supination torque tasks. Three accelerometers were employed to record the MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) at 80% maximal voluntary contraction (MVC). Signal RMS was used to quantify the amplitude of the MMG signals from a muscle, and cross-correlation coefficients were used to quantify the magnitude of the CT among muscle pairs (BB & BRA, BRA & BRD, and BB & BRD). For all investigated muscles and pairs, RMS and CT showed negligible to low negative correlations with CA, LA and ISD (r = −0.0001-−0.4611), and negligible to moderate positive correlations with ST (r = 0.004-0.511). However, almost all of these correlations were statistically insignificant (p > 0.05). These findings suggest that RMS and CT values for the elbow flexor muscles recorded and quantified using accelerometers appear invariant to anthropometric parameters. conclusion CA, LA and ISD were found to exhibit negligible to low negative correlations with RMS and CT, whereas ST exhibited negligible to moderate positive correlations with RMS and CT. These findings suggest that the MMG signal parameters and CT acquired using accelerometers appear invariant to the four anthropometric parameters but might depend on other physiological and subject-based factors. The findings obtained in this study might aid our understanding of CT mechanisms in MMG signals, which could be useful in future research for devising techniques for the reduction or removal of CT in MMG.

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Optimal spatio-temporal filter for the reduction of crosstalk in surface electromyogram

Abstract: The method can be applied to few channels, so that it is useful in applicative studies (e.g. clinics, gate analysis, rehabilitation protocols with EMG biofeedback and prosthesis control) where limited and not selective information is usually available.

Cited by 20 publications

References 33 publications

Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors

Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors

Comparison of Blind Source Separation Methods to Surface Electromyogram for Extensor Muscles of the Index and Little Fingers

Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks

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