Monsifrot Jonathan, Le Carpentier Eric, Aoustin Yannick, Farina Dario
IEEE Trans Neural Syst Rehabil Eng. 2014 Sep;22(5):1030-40. doi: 10.1109/TNSRE.2014.2316547. Epub 2014 Apr 11.
This paper addresses the sequential decoding of intramuscular single-channel electromyographic (EMG) signals to extract the activity of individual motor neurons. A hidden Markov model is derived from the physiological generation of the EMG signal. The EMG signal is described as a sum of several action potentials (wavelet) trains, embedded in noise. For each train, the time interval between wavelets is modeled by a process that parameters are linked to the muscular activity. The parameters of this process are estimated sequentially by a Bayes filter, along with the firing instants. The method was tested on some simulated signals and an experimental one, from which the rates of detection and classification of action potentials were above 95% with respect to the reference decomposition. The method works sequentially in time, and is the first to address the problem of intramuscular EMG decomposition online. It has potential applications for man-machine interfacing based on motor neuron activities.
本文探讨了肌内单通道肌电图（EMG）信号的顺序解码，以提取单个运动神经元的活动。一个隐马尔可夫模型是从EMG信号的生理产生过程推导出来的。EMG信号被描述为嵌入噪声中的几个动作电位（小波）序列的总和。对于每个序列，小波之间的时间间隔由一个参数与肌肉活动相关联的过程建模。该过程的参数与放电瞬间一起由贝叶斯滤波器顺序估计。该方法在一些模拟信号和一个实验信号上进行了测试，相对于参考分解，动作电位的检测和分类率均高于95%。该方法在时间上顺序工作，是第一个在线解决肌内EMG分解问题的方法。它在基于运动神经元活动的人机接口方面具有潜在应用。
