Weakening of synergist muscle coupling during reaching movement in stroke patients.

Background: After hemiparetic stroke, coordination of the shoulder flexor and elbow extensor muscles during a reaching movement is impaired and contributes to poor performance.

Objective: The aim was to determine whether functional coupling between electromyographic signals of synergist muscles during reaching was weakened in stroke patients who had poor motor coordination.

Methods: Surface electromyography (EMG) from the anterior deltoid, triceps brachii, biceps brachii, pectoralis major, supraspinatus, and latissimus dorsi of the affected upper limb in 11 stroke patients (mean Fugl-Meyer upper extremity score 27 ± 8) and in the dominant arm of 8 healthy controls were measured.

Single-Trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling.

Voluntary muscle fatigue is a progressive process. A recent study demonstrated muscle fatigue-induced weakening of functional corticomuscular coupling measured by coherence between the brain [electroencephalogram (EEG)] and muscle [electromyogram (EMG)] signals after a relatively long-duration muscle contraction. Comparing the EEG-EMG coherence before versus after fatigue or between data of two long-duration time blocks is not adequate to reveal the dynamic nature of the fatigue process.

Functional corticomuscular connection during reaching is weakened following stroke.

Objective: To investigate the functional connection between motor cortex and muscles, we measured electroencephalogram-electromyogram (EEG-EMG) coherence of stroke patients and controls.

Methods: Eight healthy controls and 21 patients with shoulder and elbow coordination deficits were enrolled. All subjects performed a reaching task involving shoulder flexion and elbow extension.

Nonlinear features of surface EEG showing systematic brain signal adaptations with muscle force and fatigue.

Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The purpose of this study was to examine chaotic properties of human scalp EEG signals associated with voluntary motor tasks using the largest Lyapunov exponent (L1). 64-channel scalp EEG data were recorded from eight healthy subjects in two tasks: (1) intermittent handgrip contractions at 20, 40, 60, and 80% of maximal voluntary contraction (MVC) with 20 trials at each level.