Guo Xiaoyu, Huang Subing, He Borong, Lan Chuanlin, Xie Jodie J, Lau Kelvin Y S, Takei Tomohiko, Mak Arthur D P, Cheung Roy T H, Seki Kazuhiko, Cheung Vincent C K, Chan Rosa H M
IEEE J Biomed Health Inform. 2025 Feb;29(2):1049-1061. doi: 10.1109/JBHI.2024.3453603. Epub 2025 Feb 10.
Non-negative matrix factorization (NMF), widely used in motor neuroscience for identifying muscle synergies from electromyographical signals (EMGs), extracts non-negative synergies and is yet unable to identify potential negative components (NegCps) in synergies underpinned by inhibitory spinal interneurons. To overcome this constraint, we propose to utilize rectified latent variable model (RLVM) to extract muscle synergies. RLVM uses an autoencoder neural network, and the weight matrix of its neural network could be negative, while latent variables must remain non-negative. If inputs to the model are EMGs, the weight matrix and latent variables represent muscle synergies and their temporal activation coefficients, respectively. We compared performances of NMF and RLVM in identifying muscle synergies in simulated and experimental datasets. Our simulated results showed that RLVM performed better in identifying muscle-synergy subspace and NMF had a good correlation with ground truth. Finally, we applied RLVM to a previously published experimental dataset comprising EMGs from upper-limb muscles and spike recordings of spinal premotor interneurons (PreM-INs) collected from two macaque monkeys during grasping tasks. RLVM and NMF synergies were highly similar, but a few small negative muscle components were observed in RLVM synergies. The muscles with NegCps identified by RLVM exhibited near-zero values in their corresponding synergies identified by NMF. Importantly, NegCps of RLVM synergies showed correspondence with the muscle connectivity of PreM-INs with inhibitory muscle fields, as identified by spike-triggered averaging of EMGs. Our results demonstrate the feasibility of RLVM in extracting potential inhibitory muscle-synergy components from EMGs.
非负矩阵分解（NMF）在运动神经科学中被广泛用于从肌电信号（EMG）中识别肌肉协同作用，它提取非负协同作用，但无法识别由抑制性脊髓中间神经元支持的协同作用中的潜在负成分（NegCps）。为了克服这一限制，我们建议利用整流潜变量模型（RLVM）来提取肌肉协同作用。RLVM使用自编码器神经网络，其神经网络的权重矩阵可以为负，而潜变量必须保持非负。如果模型的输入是EMG，权重矩阵和潜变量分别代表肌肉协同作用及其时间激活系数。我们比较了NMF和RLVM在模拟和实验数据集中识别肌肉协同作用的性能。我们的模拟结果表明，RLVM在识别肌肉协同作用子空间方面表现更好，而NMF与真实情况有良好的相关性。最后，我们将RLVM应用于一个先前发表的实验数据集，该数据集包括来自两只猕猴在抓握任务期间上肢肌肉的EMG和脊髓运动前中间神经元（PreM-INs）的尖峰记录。RLVM和NMF的协同作用高度相似，但在RLVM协同作用中观察到一些小的负肌肉成分。RLVM识别出的具有NegCps的肌肉在NMF识别出的相应协同作用中表现出接近零的值。重要的是，RLVM协同作用的NegCps与通过EMG的尖峰触发平均确定的具有抑制性肌肉场的PreM-INs的肌肉连接性相对应。我们的结果证明了RLVM从EMG中提取潜在抑制性肌肉协同作用成分的可行性。
