A blind source separation algorithm for decoding the mechanical spatiotemporal responses of motor units

• Published in: Science China. Technological sciences Vol. 68; no. 5
• Main Authors: Yin, Zongtian, Chen, Chen, Kang, Yiming, Ding, Han, Zhu, Xiangyang, Meng, Jianjun
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.05.2025; Springer Nature B.V
• Subjects: Algorithms; Decoding; Engineering; Image sequencing; Mechanical analysis; Muscles; Nervous system; Probability density functions; Research Paper; Spatial data; Spatiotemporal data; Ultrasonic imaging; ultrafast ultrasound; blind source separation; spatiotemporal ICA; motor units; mechanical response
• ISSN: 1674-7321; 1869-1900
• DOI: 10.1007/s11431-024-2907-y

Skeletal muscles are essential parts of the human motor system and are mainly regulated by motor units (MUs) through the nervous system. As a widely used noninvasive measurement of MUs, surface EMG cannot obtain in-depth spatial information on MUs. Ultrafast ultrasound (UUS) can measure the mechanical response of MUs from muscle morphology with image sequences. This research proposed a blind source separation method with enhanced interpretability for decoding ultrasound image sequences to obtain the mechanical response of MUs. In particular, the spatiotemporal data were decomposed using non-negative matrix factorization (NMF). Then, the spatial components’ multiple probability density functions were obtained using a parametric self-fitting function. The proposed algorithm, called NMF-stICA, was validated on ten groups of computational simulation datasets. The accuracies of the obtained spatial and temporal components were 87.26% ± 2.18% and 85.13% ± 1.83%, respectively. Further, a dynamic ultrasound phantom experiment was performed, and all the potential spatial components were correctly decoded. Additionally, isometric contraction human experiments were conducted on the biceps muscle of eight subjects with simultaneous acquisition of UUS and intramuscular electromyography (iEMG). The results showed that the rate of agreement was 58.71%, comparing the decoded components with the firing pattern of iEMG. The proposed decoding method can get precise spatial position and the firing pattern of the MUs in the skeletal muscle. This might help to study the neuromechanical properties of MUs and localize disease in specific muscle regions.