目的：本研究聚焦于个体支配单个手指伸展运动的微观神经元特征分析，得出不同个体微观神经元特征的异同性。方法：通过盲源分离算法分解出支配不同个体在单个手指伸展时的微观运动神经元，对所得神经元进行二维空间特征量化，并利用不同个体分解出的神经元特征进行手指分类，通过特征量化和分类结果来验证支配不同个体的运动神经元特征的异同性。通过共享运动神经元占比分析来研究神经支配不同手指协同运动的微观神经元特性在不同个体间的差异性。结果 ：不同个体的食指与中指的运动神经元空间分布差异较大，激活面积相似。隔人利用不同人群数据作为训练集和测试集进行手指 分类的平均准确率为86.99%，经迁移成分分析（TCA）校准后显著提高为90.07%。通过不同个体共享神经元占比分析，我们发现食指与中指无名指和小指的共享神经元占比较少，而无名指与小指占比最高。结论：不同个体控制不同手指的运动神经元整体的空间放电特征较为相似，存在较小的个体性差异。本研究揭示不同个体在进行手指运动时的内在神经机理，为手指运动障碍患者的临床神经机理分析和研究以及相关工程应用提供参考。
Objective This study focuses on the analysis of the characteristics of motoneurons controlling the extension of a single finger in different individuals, and obtains the similarity and difference of the characteristics of micro motoneurons in different individuals. Methods The The micro motoneurons were decomposed by blind source separation algorithm. The two-dimensional spatial characteristics, centroid coordinates and activation area of motoneurons were quantified. Furthermore, the similarities and differences of motoneurons in individuals during different finger movements were verified using “leaving-one-subject-out” method. In addition, the characteristics of motoneurons innervating the coordinated movement of different fingers between individuals were studied by the proportion of shared motor neurons. Results There were significant differences in the spatial distribution of motoneurons between the index finger and the middle finger for different individuals, but the activation area of them was similar. The average accuracy of finger classification was 86.99%, and significantly improved to 90.07% after using TCA calibration. Through the analysis of the proportion of shared neurons in different individuals, we found that the proportion of shared neurons in index finger and middle finger ring finger and little finger was relatively small, while the proportion of shared neurons in ring finger and little finger was the highest. Conclusion the spatial discharge characteristics of motoneurons controlling different fingers in different individuals are similar, and the motoneurons driving different fingers can be distinguished according to fingers. This study reveals the internal neural mechanism of different individuals during finger muscle contraction, which can provide a reference for the neural mechanism research of finger movement and the related engineering applications of motion assisted systems.