Improved online decomposition of non-stationary electromyogram via signal enhancement using a neuron resonance model:...

Objective. Motor unit (MU) discharge information obtained via the online electromyogram (EMG) decomposition has shown promising prospects in multiple applications. However, the nonstationarity of EMG signals caused by the rotation (recruitment-derecruitment) of MUs and the variation of MU action potentials (MUAP) can significantly degrade the online decomposition performance. This study aimed to develop an independent component analysis-based online decomposition method that can accommodate the nonstationarity of EMG signals.Approach. The EMG nonstationarity can make the separation vectors obtained beforehand inaccurate, resulting in the reduced amplitudes of the peaks corresponding to firing events in the source signal (independent component) and then the decreased accuracy of firing events. Therefore, we utilized the FitzHugh-Nagumo (FHN) resonance model to enhance the firing peaks in the source signal in order to improve the decomposition accuracy. A two-session approach was used with the offline session to extract the separation vectors and train the FHN models. In the online session, the source signal was estimated and further processed using the FHN model before detecting the firing events in a real-time manner. The proposed method was tested on simulated EMG signals, in which MU rotation and MUAP variation were involved to mimic the nonstationarity of EMG recordings.Main results. Compared with the conventional method, the proposed method can improve the decomposition accuracy significantly (88.70% ± 4.17% vs. 92.43% ± 2.79%) by enhancing the firing peaks, and more importantly, the improvement was more prominent when the EMG signal had stronger background noises (87.00% ± 3.70% vs. 91.66% ± 2.63%).Conclusions. Our results demonstrated the effectiveness of the proposed method to utilize the FHN model to improve the online decomposition performance on the nonstationary EMG signals. Further development of our method has the potential to improve the performance of the neural decoding system that utilizes the MU discharge information and promote its application in the neural-machine interface.