No abstract

Objective. Traditional training focuses on improving the motor function level of the limbs or joint levels, while inter-muscular coordination plays an important role in fine motor control and is often overlooked. The purpose of this study was to investigate the changes in inter-muscular coordination induced by the myoelectric-controlled interface (MCI) and the therapeutic effects of MCI-based inter-muscular coordination training on stroke patients. Approach. Eleven stroke patients, twenty young subjects and thirteen age-matched subjects were recruited to determine the dimensionality effect of MCI on inter-muscular coordination in the evaluation test. In addition, a stroke patient participated in a 20-day training session to test the therapeutic effects as a case study analysis in the training test. In these two tests, all subjects performed tracking tasks by flexing/extending their elbows according to the biofeedback from one-dimensional and two-dimensional (2D) MCI. Meanwhile, the electromyography and functional near infrared spectroscopy signals were recorded simultaneously to reflect the muscle and cortical activations. Main results. In all groups, as the MCI dimensionality increased, the antagonist activation decreased significantly, while the involvement in prefrontal cortex and primary motor cortex increased significantly. A significant reduction in muscle activation and an increase in cortical activation were found in the stroke patient, which might be due to a progressive normalization of patient after the training. Significance. These findings suggested that 2D MCI could be an effective tool to directly modulate inter-muscular coordination for stroke patients. Inter-muscular coordination training may restore the ability to coordinate agonist-antagonist muscle of stroke patient and this improvement may be accompanied by cortical reorganization.

Motor planning can enable integration of sensory input to generate motor execution. However, how the brain analyses visual information and modulates its signals to the muscle have been not well studied in human. The aim of this study was to investigate the dimensionality effect of myoelectric-controlled interface (MCI) on motor planning and motor execution during elbow tracking movements. Twenty right-handed healthy subjects were recruited to complete tracking tasks by modulating their biceps and triceps activation within one-dimensional and two-dimensional MCI. The electromyography (EMG) signals of the biceps and triceps was recorded to calculate the normalized muscle activation, while the functional near infrared spectroscopy (fNIRS) signals of the prefrontal cortex (PFC) and bilateral motor cortex (MC) were also collected to gain the brain activation simultaneously. The results showed that the activation of antagonist muscle was significant lower within two-dimensional MCI than that within one-dimensional MCI at the muscle level. At the brain level, it was found an obvious higher activation in the PFC and the left MC within two-dimensional MCI than that within one-dimensional MCI. The current EMG-fNIRS study confirmed that visual feedback can influence motor planning and motor execution, and the PFC and bilateral MC are the likely targeted sites of the proactive inhibition of the antagonist muscle. This study adds a new perspective to possible visual regulation of neuromuscular control, which might be an effective rehabilitation method to improve abnormal muscle coordination in the clinic.

Objective. Human movement is a complex process requiring information transmission in inter-cortical, cortico-muscular and inter-muscular networks. Though motor deficits after stroke are associated with impaired networks in the cortico-motor system, the mechanisms underlying these networks are to date not fully understood. The purpose of this study is to investigate the changes in information transmission of the inter-cortical, cortico-muscular and inter-muscular networks after stroke and the effect of myoelectric-controlled interface (MCI) dimensionality on such information transmission in each network. Approach. Fifteen healthy control subjects and 11 post-stroke patients were recruited to perform elbow tracking tasks within different dimensional MCIs in this study. Their electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) signals were recorded simultaneously. Transfer entropy was used to analyse the functional connection that represented the information transmission in each network based on the fNIRS and EMG signals. Main results. The results found that post-stroke patients showed the increased inter-cortical connection versus healthy control subjects, which might be attributed to cortical reorganisation to compensate for motor deficits. Compared to healthy control subjects, a lower strength cortico-muscular connection was found in post-stroke patients due to the reduction of information transmission following a stroke. Moreover, the increased MCI dimensionality strengthened inter-cortical, cortico-muscular and inter-muscular connections because of higher visual information processing demands. Significance. These findings not only provide a comprehensive overview to evaluate changes in the cortico-motor system due to stroke, but also suggest that increased MCI dimensionality may serve as a useful rehabilitation tool for boosting information transmission in the cortico-motor system of post-stroke patients.

Visual-motor tracking movement is a common and essential behavior in daily life. However, the contribution of future information to visual-motor tracking performance is not well understood in current research. In this study, the visual-motor tracking performance with and without future-trajectories was compared. Meanwhile, three task demands were designed to investigate their impact. Eighteen healthy young participants were recruited and instructed to track a target on a screen by stretching/flexing their elbow joint. The kinematic signals (elbow joint angle) and surface electromyographic (EMG) signals of biceps and triceps were recorded. The normalized integrated jerk (NIJ) and fuzzy approximate entropy (fApEn) of the joint trajectories, as well as the multiscale fuzzy approximate entropy (MSfApEn) values of the EMG signals, were calculated. Accordingly, the NIJ values with the future-trajectory were significantly lower than those without future-trajectory (p-value < 0.01). The smoother movement with future-trajectories might be related to the increasing reliance of feedforward control. When the task demands increased, the fApEn values of joint trajectories increased significantly, as well as the MSfApEn of EMG signals (p-value < 0.05). These findings enrich our understanding about visual-motor control with future information.