Cortical-muscular functional coupling reflects the interaction between the cerebral cortex and the muscle activities. Corticomuscular coherence (CMC) has been extensively revealed in sustained contractions of various upper- and lower-limb muscles during static and dynamic force outputs. However, it is not well-understood that the CMC modulation mechanisms, i.e., the relation between a cerebral hemisphere and dynamic motor controlling limbs at constant speeds, such as isokinetic movement. In this paper, we explore the CMC between upper arm flexors/extensors movement and motor cortex during isometric exercise and cyclically isokinetic movement. We also provide further insights of frequency-shift and the neural pathway mechanisms in isokinetic movement by evaluating the coherence between motor cortex and agonistic or antagonistic muscles. This study is the first to investigate the relationship between cortical-muscular functional connections in elbow flexion-extension movement with constant speeds. The result shows that gamma-range coherence for isokinetic movement is greatly increased compared with isometric exercise, and significant CMC is observed in the entire flexion-extension stage regardless the nature of muscles contraction, although dominant synchronization of cortical oscillation and muscular activity resonated in sustained contraction stage principally. Besides, the CMC for extensors and flexors are explicitly consistent in contraction stage during cyclically isokinetic elbow movement. It is concluded that cortical-muscular coherence can be dynamically modulated as well as selective by cognitive demands of the body, and the time-varying mechanisms of the synchronous motor oscillation exist in healthy individuals during dynamic movement.

No abstract

No abstract

It is of great importance to decode motion dynamics of the human limbs such as the joint angle and torque in order to improve the functionality and provide more intuitive control in human-machine collaborative systems. In order to achieve feasible prediction, both the surface electromyography (sEMG) and A-mode ultrasound were applied to detect muscle deformation and motor intent. Six abled subjects were recruited to perform five trails elbow isokinetic flexion and extension, and each trail contained five repetitions, with muscle deformation and sEMG signals recorded simultaneously. The experimental datasets were categorized as: the ultrasound-EMG combined datasets, ultrasound-only datasets and EMG-only datasets. The support vector machine (SVM) regression model was developed for both elbow joint angle and torque prediction, based on the above three kinds of datasets. The root-mean-square error (RMSE) and the correlation coefficients (R) were applied to evaluate the prediction accuracy. The results across all the subjects for different datasets indicated that the combined datasets and the ultrasound datasets were superior to the sEMG datasets both on elbow joint angle and torque prediction, and there were no significant differences between the combined datasets and the ultrasound datasets. It turns out that elbow angle and torque can be reconstructed by Amode ultrasound, and the significant findings pave the way towards the application of musculature-driven human-machine collaborative systems.Angle, torque, surface electromyography (sEMG), ultrasound, support vector machine (SVM), regression, isokinetic contraction.

No abstract

No abstract

No abstract

No abstract