Near- and Far-Fields: Source Characteristics and the Conducting Medium in Neurophysiology

Stegeman, Dick F.; Dumitru, Daniel; King, John C.; Roeleveld, Karin

doi:10.1097/00004691-199709000-00009

Cited by 87 publications

(69 citation statements)

References 22 publications

(28 reference statements)

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“…This dipolar moment imbalance generates a net superimposed dipole at the musculotendinous junction that manifests throughout the volume conductor as a so-called far-field potential or standing wave. 10,11,34 Because of the particular recording montage, all of the active electrodes are in the positive region of the superimposed dipole field (Fig. 4E).…”

Section: Discussionmentioning

confidence: 99%

Motor unit action potential components and physiologic duration

Dumitru

King

Rogers³

1999

Muscle Nerve

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Section: Discussionmentioning

confidence: 99%

Motor unit action potential components and physiologic duration

Dumitru

King

Rogers³

1999

Muscle Nerve

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“…The first reflects propagating MUAPs along the muscle fibers and the second the nonpropagating end-of-fiber effects arising when the MUAPs meet the muscle-tendon junction (Stegeman et al 1997). Importantly, the currents and potentials associated with these two phenomena differ.…”

Section: Contribution Of End-of-fiber Effects To Monopolar Semgmentioning

confidence: 99%

Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography

Piitulainen

Botter

Bourguignon

et al. 2015

Journal of Neurophysiology

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muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm ϫ 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic "macroscopic" monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91-95% of the HD-sEMG channels, with maximum coherence at ϳ25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19 -27% stronger for HD-sEMG than for "macroscopic" monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ϳ12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.

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“…4, eigenvector of mode 1], having peak values at the upper left corner of the EMG grid. Accordingly, the monopolar EMG signals were highly correlated, which occurs because monopolar EMG consists to large extent of so-called far-field activity [18], possibly even from motor units of (distant) other muscles. In terms of force prediction, the first mode showed a substantially higher RMSD compared to the monopolar signal itself [ Fig.…”

Section: B Principal Component Analysismentioning

confidence: 99%

“…However, at least the modes at both ends of the spectrum appear to have an interpretable structure. Mode 1 may be interpreted as common background or weighted mean of the original monopolar EMG determined by far field potentials [18]. Recall that this mode accounted for about 90% [Fig.…”

Section: B Principal Component Analysismentioning

confidence: 99%

Improving EMG-Based Muscle Force Estimation by Using a High-Density EMG Grid and Principal Component Analysis

Staudenmann

Kingma

Daffertshofer

et al. 2006

IEEE Trans. Biomed. Eng.

149

154

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Abstract-The accuracy of predictions of muscle force based on electromyography (EMG) is an important issue in biomechanics and kinesiology. Since human skeletal muscles show a high diversity and heterogeneity in their fiber architecture, it is difficult to properly align electrodes to the muscle fiber direction. Against this background, we analyzed the effect of different bipolar configuration directions on EMG-based force estimation. In addition, we investigated whether principal component analysis (PCA) can improve this estimation. High-density surface-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) between predicted and measured force was determined. We found the best bipolar configuration direction to cause a 13% lower RMSD relative to the worst direction. Optimal results were obtained with electrodes aligned with the expected main muscle fiber direction. We found that PCA reduced RMSD by about 40% compared to conventional bipolar electrodes and by about 12% compared to optimally aligned multiple bipolar electrodes. Thus, PCA contributes to the accuracy of EMG-based estimation of muscle force when using a high-density EMG grid.Index Terms-Force estimation, heterogeneous muscle fiber architecture, human, principal component analysis, redundancy, surface electromyography.

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Near- and Far-Fields: Source Characteristics and the Conducting Medium in Neurophysiology

Abstract: N e a r -a n d F a r -F ie ld s : S o u r c e C h a r a c te r is tic s a n d th e C o n d u c tin g M e d iu m in N e u r o p h y s io lo g y

Cited by 87 publications

References 22 publications

Motor unit action potential components and physiologic duration

Motor unit action potential components and physiologic duration

Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography

Improving EMG-Based Muscle Force Estimation by Using a High-Density EMG Grid and Principal Component Analysis

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