Motor control is a complex process of coordination and information interaction among neural, motor, and sensory functions. Investigating the correlation between motor-physiological information helps to understand the human motor control mechanisms and is important for the assessment of motor function status.

Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G.

Coherence fails to account for the inherently multi-time scales-based correlations. In this paper, we proposed a multiscale-like coherence model, defined as composite multiscale coherence (CMSC). Both simulation and experimental data expound the effectiveness of the proposed multiscale-like model to describe the multiscale correlation between two time series. This study extends the relative researches on the FCMC to the multi-time scale.

Motor control is a complex process of coordination and information interaction among neural, motor, and sensory functions. Investigating the correlation between motor-physiological information helps to understand the human motor control mechanisms and is important for the assessment of motor function status.

Efficient rehabilitation state evaluation is important to the design of rehabilitation strategies after stroke. However, most traditional evaluations have depended on subjective clinical scales, which do not entail quantitative evaluation of the motor function. Functional corticomuscular coupling (FCMC) can be used to quantitatively describe the rehabilitation state. However, how to apply FCMC to clinical evaluation still needs to be studied.