How computational technique and spike train properties affect coherence detection

Terry, Kevin; Griffin, Lisa

doi:10.1016/j.jneumeth.2007.09.014

Cited by 34 publications

(33 citation statements)

References 32 publications

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“…j=1 P xx j L j p j=1 P yy j L j estimation, both for analysis between two muscles and for pooled analysis, was based on the use of overlapping segments (Welch 1967), for two main reasons: (1) selection of the synergy activation periods significantly reduces the length of the analyzed time series, and (2) the use of overlapping segments has been shown to improve coherence estimation (Terry and Griffin 2008). Each coherence spectrum was thus estimated with 500-ms Hanning windows with 50 % overlapping, leading to a spectral resolution of 2 Hz and doubling up the number of available segments.…”

Section: Emg-emg Coherence Among Synergistic Musclesmentioning

confidence: 99%

Intermuscular coherence contributions in synergistic muscles during pedaling

et al. 2015

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The execution of rhythmical motor tasks requires the control of multiple skeletal muscles by the Central Nervous System (CNS), and the neural mechanisms according to which the CNS manages their coordination are not completely clear yet. In this study, we analyze the distribution of the neural drive shared across muscles that work synergistically during the execution of a free pedaling task. Electromyographic (EMG) activity was recorded from eight lower limb muscles of eleven healthy untrained participants during an unconstrained pedaling exercise. The coordinated activity of the lower limb muscles was described within the framework of muscle synergies, extracted through the application of nonnegative matrix factorization. Intermuscular synchronization was assessed by calculating intermuscular coherence between pairs of EMG signals from co-active, both synergistic and non-synergistic muscles within their periods of co-activation. The spatiotemporal structure of muscle coordination during pedaling was well represented by four muscle synergies for all the subjects. Significant coherence values within the gamma band (30-60 Hz) were identified only for one out of the four extracted muscle synergies. This synergy is mainly composed of the activity of knee extensor muscles, and its function is related to the power production and crank propelling during the pedaling cycle. In addition, a significant coherence peak was found in the lower frequencies for the GAM/SOL muscle pair, possibly related to the ankle stabilizing function of these two muscles during the pedaling task. No synchronization was found either for the other extracted muscle synergies or for pairs of co-active but non-synergistic muscles. The obtained results seem to suggest the presence of intermuscular synchronization only when a functional force production is required, with the observed gamma band contribution possibly reflecting a cortical drive to synergistic muscles during pedaling.

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Section: Emg-emg Coherence Among Synergistic Musclesmentioning

confidence: 99%

Intermuscular coherence contributions in synergistic muscles during pedaling

et al. 2015

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“…An independent component analysis was used, when required, to remove artifacts without trial exclusion. Right and left SMC-EEG and rectified EMG signals from ROP and LOP were segmented into epochs of 512 ms and Hanning-windowed to control spectral leakage (Terry and Griffin, 2008), and the Welch procedure (Welch, 1967) with 50% overlap was used. The PSD depends on the number of synchronously oscillating neurons and their degree of synchrony at a defined frequency.…”

Section: Estimation Of Power Spectral Density and Cmcmentioning

confidence: 99%

“…CMC was estimated using the methods of Terry and Griffin (2008). The upper 95% confidence limit for a CMC of zero was used to determine significant results (Terry and Griffin, 2008).…”

Section: Estimation Of Power Spectral Density and Cmcmentioning

confidence: 99%

“…The upper 95% confidence limit for a CMC of zero was used to determine significant results (Terry and Griffin, 2008). The frequency and amplitude of the significant CMC peaks were determined.…”

Section: Estimation Of Power Spectral Density and Cmcmentioning

confidence: 99%

“…4 A). A 30 s window was used since this has been shown to be the minimum time to provide reliable coherence values (Terry and Griffin, 2008). For stationarity estimate, we used the bipolar derivation with maximal CMC selected for the whole-period analysis, as above (Fig.…”

Section: Stationarity Of Cmc: "Sliding Window Analysis"mentioning

confidence: 99%

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Developmental Tuning and Decay in Senescence of Oscillations Linking the Corticospinal System

Graziadio¹,

Basu²,

Tomasevic³

et al. 2010

J. Neurosci.

101

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There is increasing evidence of the importance of synchronous activity within the corticospinal system for motor control. We compared oscillatory activity in the primary sensorimotor cortex [EEG of sensorimotor cortex (SMC-EEG)] and a motor neuronal pool [surface electromyogram of opponens pollicis (OP-EMG)], and their coherence in children (4 -12 years of age), young adults (20 -35 years of age), and elderly adults (Ͼ55 years of age). The ratio between lower (2-13 Hz) and higher (14 -32 Hz) frequencies in both SMC-EEG and OP-EMG decreased with age, correlating inversely with motor performance. There was evidence for larger, more distributed cortical networks in the children and elderly compared with young adults. Corticomuscular coherence (CMC) was present in all age groups and shifted between frontal and parietal cortical areas. In children, CMC was smaller and less stationary in amplitude and frequency than in adults. Young adults had single peaks of CMC clustered near the modal frequency (23 Hz); multiple peaks with a broad spread of frequencies occurred in children and the elderly; the further the frequency of the maximum peak CMC was from 23 Hz, the poorer the performance. CMC amplitude was inversely related to performance in young adults but was not modulated in relation to performance in children and the elderly. We propose that progressive fine-tuning of the frequency coding and stabilization of the dynamic properties within and between corticospinal networks occurs during adolescence, refining the capacity for efficient dynamic communication in adulthood. In old age, blurring of the tuning between networks and breakdown in their integration occurs and is likely to contribute to a decrement in motor control.

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Fatigue related changes in electromyographic coherence between synergistic hand muscles

Kattla

Lowery

2009

Exp Brain Res

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The aim of this study was to examine coherence between surface electromyographic (EMG) signals from two index finger flexor muscles, the first dorsal interosseous (FDI) and flexor digitorum superficialis (FDS), during and immediately following sustained, fatiguing isometric contraction. Coherence was observed between the FDI and FDS EMG signals in the tremor (8-12 Hz), beta (15-35 Hz) and gamma (35-60 Hz) bands in all subjects. A significant increase in EMG-EMG coherence in the beta and gamma frequency bands was observed immediately following the fatiguing contraction. No significant difference was observed in the tremor band coherence before and after fatigue. Coherence was observed between EMG and force in the tremor band during both the pre- and post-fatigue contractions and a significant increase in the FDI EMG-force coherence post-fatigue was observed. It is suggested that the increase in beta and gamma band coherence with fatigue may be due to increased levels of corticomotoneuronal drive to both muscles. Alternatively, the increased EMG-EMG coherence may reflect an increased contribution of peripheral afferents to coupling across the muscle with fatigue. Although the functional significance is not clear, the increase in coherence may help to overcome reduced motoneuron excitability with fatigue, to bind together different sensorimotor elements or to coordinate force generation across muscles in a more synergistic manner as the force generating capacity of the muscle is decreased.

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How computational technique and spike train properties affect coherence detection

Cited by 34 publications

References 32 publications

Intermuscular coherence contributions in synergistic muscles during pedaling

Intermuscular coherence contributions in synergistic muscles during pedaling

Developmental Tuning and Decay in Senescence of Oscillations Linking the Corticospinal System

Fatigue related changes in electromyographic coherence between synergistic hand muscles

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