IEEE Trans Neural Syst Rehabil Eng. 2019 Jul;27(7):1483-1491. doi: 10.1109/TNSRE.2019.2922453. Epub 2019 Jun 13.
We introduce two novel methods for the estimation of muscle excitation from surface electromyograms (EMGs), the so called cumulative motor unit activity index (CAI) and robust CAI (rCAI). Both methods aim to remove the detected motor unit action potential (MUAP) contributions from EMG but do not assess the individual motor unit spike trains. Instead, they directly estimate the cumulative motor unit spike train (CST). We compared the methods with the spatially averaged root-mean-square (RMS) envelope of the EMG signals and with the CST, estimated by the previously introduced convolution kernel compensation (CKC) method. The tests on synthetic EMG with known muscle excitation profiles demonstrated superior accuracy of newly introduced methods. In the case of 64 EMG channels and 20-dB noise, the RMS, CAI, rCAI, and CKC estimators, calculated on 0.125-s-long signal epochs, yielded the normalized RMS error (NRMSE) of 14.5% ± 2.8%, 4.4% ± 3.2%, 4.1% ± 1.8%, and 6.3% ± 4.6%, respectively. In the experimental signals from wrist extensors and flexors, the RMS, CAI, rCAI, and CKC estimations were compared to exerted muscle force. When calculated on 0.125-s-long signal epochs, they yielded the NRMSE of 11.2% ± 3.5%, 8% ± 5.6%, 10.7% ± 6.8%, and 9.0% ± 4.9%, respectively. Therefore, the newly introduced methods exhibit accuracy that is comparable to at least 200-times slower CKC method.
我们介绍了两种从表面肌电图（EMG）估计肌肉兴奋的新方法，即所谓的累积运动单位活动指数（CAI）和稳健 CAI（rCAI）。这两种方法都旨在从 EMG 中去除检测到的运动单位动作电位（MUAP）贡献，但不评估单个运动单位的尖峰序列。相反，它们直接估计累积运动单位尖峰序列（CST）。我们将这些方法与 EMG 信号的空间平均均方根（RMS）包络以及以前介绍的卷积核补偿（CKC）方法估计的 CST 进行了比较。具有已知肌肉兴奋分布的合成 EMG 的测试表明，新引入的方法具有更高的准确性。在 64 个 EMG 通道和 20dB 噪声的情况下，在 0.125s 长的信号段上计算的 RMS、CAI、rCAI 和 CKC 估计值，其归一化均方根误差（NRMSE）分别为 14.5%±2.8%、4.4%±3.2%、4.1%±1.8%和 6.3%±4.6%。在来自腕伸肌和屈肌的实验信号中，RMS、CAI、rCAI 和 CKC 估计值与肌肉用力进行了比较。在 0.125s 长的信号段上计算时，它们的 NRMSE 分别为 11.2%±3.5%、8%±5.6%、10.7%±6.8%和 9.0%±4.9%。因此，新引入的方法具有与至少 200 倍慢的 CKC 方法相当的准确性。
