Hand Functional Impairment in Stroke Survivors Using Coherence Analysis

Individuals with a stroke may experience varying degrees of nervous system damage leading to motor impairments, especially hand functional impairment. Precise assistive robot therapy has proven effective for motor rehabilitation of stroke survivors. To ensure its effectiveness, understanding the origins and assessing the severity of these impairments are crucial. In this preliminary study, we conducted the coherence analysis on high-density electromyographic (HD-EMG) signals to extract information on motor unit (MU) spike trains. Five stroke subjects and five age-matched neurologically intact control subjects participated in the experiment. A motor unit decomposition approach was used to obtain a group of concurrently active motor units across muscle groups and muscle compartments. The shared input across muscle groups or muscle compartments was quantified by a discharge-timing coherence analysis. The coherence at different frequency bandwidths with well-defined physiological origins allowed us to distinguish the different origins of diffused input in the central nervous system. Our results revealed varying degrees of increased coherence in the alpha (8-12Hz), beta (1530Hz), and gamma (30-60Hz) bands between flexor digitorum superficialis (FDS) compartments, extensor digitorum (ED) compartments, and different intrinsic muscles on the affected sides, in comparison to the contralateral sides and intact control subjects. These findings indicate an increased shared synaptic input to the motor neuron pool on the affected sides, originating from different levels, including spinal and supraspinal pathways. Our study verifies the feasibility of using coherence analysis to better understand the origins and severity of motor impairment, thus contributing to the development of assistive robot therapy for stroke survivors.