IntroductionUpper limb rehabilitation assessment plays a pivotal role in the recovery process of stroke patients. The current clinical assessment tools often rely on subjective judgments of healthcare professionals. Some existing research studies have utilized physiological signals for quantitative assessments. However, most studies used single index to assess the motor functions of upper limb. The fusion of surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) presents an innovative approach, offering simultaneous insights into the central and peripheral nervous systems.MethodsWe concurrently collected sEMG signals and brain hemodynamic signals during bilateral elbow flexion in 15 stroke patients with subacute and chronic stages and 15 healthy control subjects. The sEMG signals were analyzed to obtain muscle synergy based indexes including synergy stability index (SSI), closeness of individual vector (CV) and closeness of time profile (CT). The fNIRS signals were calculated to extract laterality index (LI).ResultsThe primary findings were that CV, SSI and LI in posterior motor cortex (PMC) and primary motor cortex (M1) on the affected hemisphere of stroke patients were significantly lower than those in the control group (p < 0.05). Moreover, CV, SSI and LI in PMC were also significantly different between affected and unaffected upper limb movements (p < 0.05). Furthermore, a linear regression model was used to predict the value of the Fugl-Meyer score of upper limb (FMul) (R2 = 0.860, p < 0.001).DiscussionThis study established a linear regression model using force, CV, and LI features to predict FMul scale values, which suggests that the combination of force, sEMG and fNIRS hold promise as a novel method for assessing stroke rehabilitation.

中文翻译：

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基于 sEMG 和 fNIRS 的力量、肌肉激活和半球间平衡指数对中风的上肢运动进行评估

引言上肢康复评估在脑卒中患者的康复过程中起着举足轻重的作用。目前的临床评估工具往往依赖于医疗保健专业人员的主观判断。一些现有的研究已经利用生理信号进行定量评估。然而，大多数研究采用单一指标来评估上肢运动功能。表面肌电图 (sEMG) 和功能性近红外光谱 (fNIRS) 的融合提出了一种创新方法，可同时洞察中枢和周围神经系统。方法我们同时收集 15 名中风患者双侧肘部屈曲时的 sEMG 信号和脑血流动力学信号具有亚急性和慢性阶段以及 15 名健康对照受试者。分析表面肌电信号以获得基于肌肉协同的指标，包括协同稳定性指数（SSI），个体向量的接近度（CV）和时间剖面的接近度（C时间）。计算 fNIRS 信号以提取偏侧性指数（李). 结果主要发现是CV,SSI和李脑卒中患者患侧半球的后运动皮层（PMC）和初级运动皮层（M1）的含量显着低于对照组。p< 0.05）。而且，CV,SSI和李受影响和未受影响的上肢运动之间的 PMC 也存在显着差异（p< 0.05）。此外，使用线性回归模型来预测上肢Fugl-Meyer评分（FMul）的值（右2= 0.860,p< 0.001).讨论本研究建立了一个使用力的线性回归模型，CV， 和李预测 FMul 量表值的特征，这表明力、sEMG 和 fNIRS 的组合有望成为评估中风康复的新方法。