Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor - Dai, Chenyun; Zheng, Yang; Hu, Xiaogang (Frontiers in Neurology, 2018)
Individuals with a hemispheric stroke tend to show persistent impairment in their affected hand. The impaired muscle activation poses a challenge for robust decoding of intended movement that can be used for robotic therapies. Robotic assistant based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the extracted decoded descending neural drive through discharge information of the motoneuron poolpopulation motoneuron discharge timings. Motoneuron discharge eventss were obtained by decomposing the high-density surface electromyogram signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the traditional classic myoelectric-based estimation. Our results showed that the neural-drive-based approach had a better performancecan better predict the force output, quantified by (lower estimation errors and higher correlations with the actual muscle force), compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based estimation approach can potentially be used as a lead to more robust interface signal for robotic therapies for during stroke rehabilitation.