IEEE Trans Biomed Eng. 2022 Feb;69(2):746-757. doi: 10.1109/TBME.2021.3104621. Epub 2022 Jan 20.
Real-time intramuscular electromyography (iEMG) decomposition, as an identification procedure of individual motor neuron (MN) discharge timings from a streaming iEMG recording, has the potential to be used in human-machine interfacing. However, for these applications, the decomposition accuracy and speed of current approaches need to be improved.
In our previous work, a real-time decomposition algorithm based on a Hidden Markov Model of EMG, using GPU-implemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs), was proposed. In this paper, a substantially extended version of this algorithm that boosts the accuracy while maintaining real-time implementation, is introduced. Specifically, multiple heuristics that aim at resolving the problems leading to performance degradation, are applied to the original model. In addition, the recursive maximum likelihood (RML) estimator previously used to estimate the statistical parameters of the spike trains, is replaced by a linear regression (LR) estimator, which is computationally more efficient, in order to ensure real-time decomposition with the new heuristics.
The algorithm was validated using twenty-one experimental iEMG signals acquired from the tibialis anterior muscle of five subjects by fine wire electrodes. All signals were decomposed in real time. The decomposition accuracy depended on the level of muscle activation and was when less than 10 MUs were identified, substantially exceeding previous real-time results.
Single channel iEMG signals can be very accurately decomposed in real time with the proposed algorithm.
The proposed highly accurate algorithm for single-channel iEMG decomposition has the potential of providing neural information on motor tasks for human interfacing.
实时肌内电（iEMG）分解作为一种从流式 iEMG 记录中识别个体运动神经元（MN）放电时间的方法，具有用于人机接口的潜力。然而，对于这些应用，需要提高当前方法的分解准确性和速度。
在我们之前的工作中，提出了一种基于 EMG 隐马尔可夫模型的实时分解算法，使用 GPU 实现的贝叶斯滤波器来估计运动单位（MU）的尖峰列车及其动作电位（MUAP）。在本文中，介绍了一个大大扩展的版本，该版本提高了准确性，同时保持了实时实现。具体来说，应用了多个旨在解决导致性能下降的问题的启发式方法来改进原始模型。此外，之前用于估计尖峰列车统计参数的递归最大似然（RML）估计器被计算效率更高的线性回归（LR）估计器所取代，以确保新启发式下的实时分解。
该算法使用五个受试者的胫骨前肌通过细金属丝电极采集的二十一个实验 iEMG 信号进行了验证。所有信号都实时分解。分解准确性取决于肌肉激活水平，当识别的 MU 少于 10 个时，大大超过了以前的实时结果。
所提出的算法可以非常准确地实时分解单通道 iEMG 信号。
用于单通道 iEMG 分解的高准确性算法具有为人机接口提供有关运动任务的神经信息的潜力。
