IEEE Trans Neural Syst Rehabil Eng. 2022;30:2012-2021. doi: 10.1109/TNSRE.2022.3192272. Epub 2022 Jul 26.
The surface electromyography (EMG) decomposition techniques have shown promising results in neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, current decomposition methods could only decode a limited number of motor units (MUs) because of the local convergence. The number of identified MUs remains similar even though more muscles or movements are involved, where multiple motor neuron populations are activated. The objective of this study was to develop a segment-wise decomposition strategy to increase the number of MU decoded from multiple motor neuron populations.
The EMG signals were divided into several segments depending on the number of involved movements. The motor neurons, activated during each movement, were regarded as a population. The convolution kernel compensation (CKC) method was applied individually for each segment to decode the motor unit discharges from each motor neuron population. The MU filters were obtained in each segment and filtrated to estimate the MU spike trains (MUSTs) from the global EMG signals. The decomposition performance was validated on synthetic and experimental EMG signals.
From synthetic EMG signals generated by two motor neuron populations, the proposed segment-wise CKC (swCKC) decoded significantly more MUs during low and medium excitation levels, with an increased rate of 16.3% to 75.4% compared with the conventional CKC. From experimental signals recorded during ten motor tasks, 133±24 MUs with the pulse-to-noise ratio of 36.6±6.5 dB were identified for each subject by swCKC, whereas the conventional CKC identified only 43±12 MUs.
These results indicate the feasibility and superiority of the proposed swCKC to decode MU activities across motor neuron populations, extending the potential applications of EMG decomposition for neural decoding during multiple motor tasks.
表面肌电图（EMG）分解技术在神经生理研究、临床诊断和人机接口方面显示出了很有前景的结果。然而，由于局部汇聚，当前的分解方法只能解码有限数量的运动单位（MU）。即使涉及更多的肌肉或运动，也会激活多个运动神经元群体，被识别的 MU 数量仍然保持不变。本研究的目的是开发一种分段分解策略，以增加从多个运动神经元群体解码的 MU 数量。
根据所涉及的运动数量，将 EMG 信号分成几个部分。在每个运动中激活的运动神经元被视为一个群体。将卷积核补偿（CKC）方法分别应用于每个部分，以从每个运动神经元群体解码运动单位放电。在每个部分中获得 MU 滤波器，并对其进行滤波，以从全局 EMG 信号估计 MU 尖峰序列（MUST）。在合成和实验 EMG 信号上验证了分解性能。
从两个运动神经元群体生成的合成 EMG 信号中，所提出的分段 CKC（swCKC）在低中和中等激发水平下显著解码了更多的 MU，与传统的 CKC 相比，增加率为 16.3%至 75.4%。从十位受试者在十种运动任务中记录的实验信号中，swCKC 为每位受试者识别出 133±24 个 MU，脉冲噪声比为 36.6±6.5dB，而传统 CKC 仅识别出 43±12 个 MU。
这些结果表明，所提出的 swCKC 用于解码跨运动神经元群体的 MU 活动具有可行性和优越性，扩展了 EMG 分解在多运动任务期间神经解码的潜在应用。
