Optimal imaging of cortico-muscular coherence through a novel regression technique based on multi-channel EEG and un-rectified EMG

Bayraktaroğlu, Zübeyir; Carlowitz-Ghori, Katherina von; Losch, Florian; Nolte, Guido; Curio, Gabriel; Nikulin, Vadim V.

doi:10.1016/j.neuroimage.2011.04.071

Cited by 43 publications

(47 citation statements)

References 36 publications

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“…Their results suggested that EMG rectification possibly improved the identification of motor unit firing rate; however, there was no significant difference between using rectified and non-rectified EMG for corticomuscular coherence estimation. The similar results were also provided by Bayraktaroglu et al (2011). In agreement with these previous findings, we find no significant difference in linear corticomuscular coupling estimation between using rectified and non-rectified EMG.…”

Section: Discussionsupporting

confidence: 90%

“…Rectification of EMG is thought to improve the detection of beta-band corticomuscular coherence (Halliday et al, 1995; Myers et al, 2003; Farina et al, 2013), while several studies argued that rectification is a nonlinear process that distort the EMG spectrum (Neto and Christou, 2010; McClelland et al, 2012). Nevertheless, recent studies demonstrated that there was no difference in coherence estimates between rectified and non-rectified EMG (Yao et al, 2007; Bayraktaroglu et al, 2011). Notably, all these studies focused on the linear corticomuscular coherence.…”

Section: Methodsmentioning

confidence: 98%

“…Significance of the n:m coherence values was determined by a permutation test (Bayraktaroglu et al, 2011), where we randomly shuffled EMG segments with respect to EEG segments. This approach is better than using a threshold obtained from two independent white noise signals, whose spectra are different from EEG/EMG signals.…”

Section: Methodsmentioning

confidence: 99%

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Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion

Yang

Solis‐Escalante

Ruit

et al. 2016

Front. Comput. Neurosci.

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Coupling between cortical oscillations and muscle activity facilitates neuronal communication during motor control. The linear part of this coupling, known as corticomuscular coherence, has received substantial attention, even though neuronal communication underlying motor control has been demonstrated to be highly nonlinear. A full assessment of corticomuscular coupling, including the nonlinear part, is essential to understand the neuronal communication within the sensorimotor system. In this study, we applied the recently developed n:m coherence method to assess nonlinear corticomuscular coupling during isotonic wrist flexion. The n:m coherence is a generalized metric for quantifying nonlinear cross-frequency coupling as well as linear iso-frequency coupling. By using independent component analysis (ICA) and equivalent current dipole source localization, we identify four sensorimotor related brain areas based on the locations of the dipoles, i.e., the contralateral primary sensorimotor areas, supplementary motor area (SMA), prefrontal area (PFA) and posterior parietal cortex (PPC). For all these areas, linear coupling between electroencephalogram (EEG) and electromyogram (EMG) is present with peaks in the beta band (15–35 Hz), while nonlinear coupling is detected with both integer (1:2, 1:3, 1:4) and non-integer (2:3) harmonics. Significant differences between brain areas is shown in linear coupling with stronger coherence for the primary sensorimotor areas and motor association cortices (SMA, PFA) compared to the sensory association area (PPC); but not for the nonlinear coupling. Moreover, the detected nonlinear coupling is similar to previously reported nonlinear coupling of cortical activity to somatosensory stimuli. We suggest that the descending motor pathways mainly contribute to linear corticomuscular coupling, while nonlinear coupling likely originates from sensory feedback.

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Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 98%

See 1 more Smart Citation

Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion

Yang

Solis‐Escalante

Ruit

et al. 2016

Front. Comput. Neurosci.

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show abstract

“…3a, b, respectively). Although fullwave rectification of raw EMG signal has been used in some studies (Halliday et al 1995;Myers et al 2003;Halliday and Farmer 2010), it has been argued that rectification compromises identification of oscillatory features of EMG (Pinto Neto and Christou 2010;Bayraktaroglu et al 2011;McClelland et al 2012) and unrectified EMG was used here.…”

Section: Coherence Analysismentioning

confidence: 99%

Cortical activity differs between position- and force-control knee extension tasks

et al. 2015

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Neural control differs between position- and force-control tasks as evident from divergent effects of fatigue and pain. Unlike force-control tasks, position-control tasks focus on a postural goal to maintain a joint angle. Cortical involvement is suggested to be less during postural control, but whether this differs between position- and force-control paradigms remains unclear. Coherence estimates the functional communication between spatially distinct active regions within the cortex (cortico-cortical coherence; CCC) and between the cortex and muscles (corticomuscular coherence; CMC). We investigated whether cortical involvement differed between force-control and more posturally focused, position-control tasks. Seventeen adults performed position- and force-control knee extensor efforts at a submaximal load (10 % maximum voluntary contraction). Surface electromyography was recorded from the right knee extensor and flexor muscles and brain activity using electroencephalography (EEG). CCC and CMC in the beta (13-30 Hz) and gamma (30-45 Hz) frequency bands were calculated between combinations of intra- and inter-hemispheric pairs of electrodes, and between four EEG electrodes that approximated the left motor cortical area, and right knee extensor EMG, respectively. Differences in EEG power and muscle activity were also calculated. CCC was greater across distributed regions in the force-control task. Beta EEG power in the left hemisphere was higher for the position-control task. Although averaged CMC data differed between tasks, there was no task difference for individual CMC data. Muscle activity and force did not differ between tasks. The results demonstrate differential cortical contributions to control force- versus position-control tasks. This might contribute to differences in performance outcomes of these tasks that have been shown previously.

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“…For coherence estimation we used the R-CMC method (Bayraktaroglu et al, 2011) which finds the spatial filter for multi-channel EEG data that maximally explains EMG activity in a given frequency range. The method is applied on narrowly bandpass-filtered data and consists of two steps.…”

Section: Cmc Estimationmentioning

confidence: 99%

Voluntary control of corticomuscular coherence through neurofeedback: A proof-of-principle study in healthy subjects

et al. 2015

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Optimal imaging of cortico-muscular coherence through a novel regression technique based on multi-channel EEG and un-rectified EMG

Cited by 43 publications

References 36 publications

Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion

Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion

Cortical activity differs between position- and force-control knee extension tasks

Voluntary control of corticomuscular coherence through neurofeedback: A proof-of-principle study in healthy subjects

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