肌肉通过招募不同数量的运动单位(MU)来产生不同水平的力,随着力量的增加,招募的MU数量逐渐上升。然而,当前的解码方法在保持MU数量的稳定和一致的增长趋势中遇到困难,其中力量增加。在某些情况下,随着力的增强,甚至可以观察到MU数量的意外减少。为了解决这个问题,在这项研究中,我们提出了一种自适应重用MU滤波器的增强解码方法。具体来说,除了正常的解码过程,我们引入了一个额外的过程,其中MU过滤器被重用来初始化算法。MU滤波器被迭代并适应新信号,旨在解码实际激活但由于严重叠加而无法识别的电机单元。我们在模拟和实验表面肌电图(sEMG)信号上测试了我们的方法。我们使用实验记录的前臂肌肉的MU动作电位模拟了具有已知MU放电模式的等距信号(10%-70%),并将分解结果与两种基线方法进行了比较:卷积核补偿(CKC)和快速独立分量分析(fastICA)。我们的方法将CKC和fastICA的解码MU数增加了135.4%±62.5%和63.6%±20.2%,分别,跨不同的信噪比。使用增强方法分解的MU的灵敏度和精度保持在与正常解码的MU相同的精度水平(p<0.001)。对于实验信号,八名健康受试者在五种不同的力量水平(10%-90%)下进行手部运动,在此期间记录和分解sEMG信号。结果表明,增强过程使所有受试者的解码MU的数量增加了21.8%±10.9%。我们讨论了通过适当地重复使用先前解码的MU滤波器并在不同的激励水平上改善激活的电机单元数量的平衡来完全捕获所有激活的电机单元的可能性。
Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected reduction in the number of MUs can even be observed as force intensifies. To address this issue, in this study, we propose an enhanced decoding method that adaptively reutilizes MU filters. Specifically, in addition to the normal decoding process, we introduced an additional procedure where MU filters are reused to initialize the algorithm. The MU filters are iterated and adapted to the new signals, aiming to decode motor units that were actually activated but cannot be identified due to heavy superimposition. We tested our method on both simulated and experimental surface electromyogram (sEMG) signals. We simulated isometric signals (10%-70%) with known MU firing patterns using experimentally recorded MU action potentials from
forearm muscles and compared the decomposition results to two baseline approaches: convolution kernel compensation (CKC) and fast independent component analysis (fastICA). Our method increased the decoded MU number by a rate of 135.4% ± 62.5 % and 63.6% ± 20.2 % for CKC and fastICA, respectively, across different signal-to-noise ratios. The sensitivity and precision for MUs decomposed using the enhanced method remained at the same accuracy level (p <0.001) as those of normally decoded MUs. For the experimental signals, eight healthy subjects performed hand movements at five different force levels (10%-90%), during which sEMG signals were recorded and decomposed. The results indicate that the enhanced process increased the number of decoded MUs by 21.8% ± 10.9 % across all subjects. We discussed the possibility of fully capturing all activated motor units by appropriately reusing previously decoded MU filters and improving the balance of activated motor unit numbers across varying excitation levels.