A Novel and Efficient Surface Electromyography Decomposition Algorithm Using Local Spatial Information

Motor unit spike trains (MUSTs) decomposed from surface electromyography (sEMG) have been an emerging solution for neural interfacing, especially for the control of upper limb prosthetics. Accurate and efficient decomposition techniques are essential and desirable. However, most decomposition methods are designed for motor units (MUs) with global maximum of single or large muscle, while in general forearm muscles are usually small and slender with low global energy. Thus, we propose a novel approach using local spatial information towards more accurate and efficient sEMG decomposition of forearm muscles. A fast spatial spike detection method is proposed to replace the time-consuming iteration process of blind source separation (BSS) methods. Here, spatial distribution characteristics of motor unit action potential are leveraged to pre-classify the candidate MUs, and further to create initial MU templates, aiming to avoid repeating convergence to high-energy MUs. The results of both simulated and experimental sEMG signals show that low-energy MUs from small muscles are more easily found compared with conventional BSS algorithm. Specifically, the proposed method can identify more 40% reliable MUs while only 30% consuming time are needed. The outcomes provide a novel solution for more efficient sEMG decomposition, potentially paving the way of MUST-based non-invasive neural interface.

Authors

• Yang Xu Purdue University
• Yang Yu School of Artificial Intelligence, Jilin University, Changchun Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University, Changchun
• Miaojuan Xia
• Xinjun Sheng
• Xiangyang Zhu Shanghai AI Laboratory

Keywords

• Electromyography
• Electrodes
• Muscles
• Bioinformatics
• Neurons
• Correlation
• Computational complexity
• Local Information
• Spatial Information
• Surface Electromyography
• Decomposition Algorithm
• Efficient Decomposition
• Local Spatial Information
• Detection Methods
• Iterative Process
• Motor Unit
• Large Muscle
• Small Muscle
• Forearm Muscles
• Blind Source Separation
• Spike Detection
• sEMG Signals
• Simulated Data
• Low Complexity
• Clustering Method
• Time Complexity
• Raw Signal
• Number Of Motor Units
• Excited Levels
• Signal-to-interference Ratio
• Template Matching Method
• Electromyography Signals
• Motor Unit Firing
• Central Electrodes
• FastICA
• Isometric Contraction
• Decomposition Results
• Motor unit decomposition
• multichannel surface EMG
• Humans
• Muscle, Skeletal
• Forearm
• Algorithms
• Action Potentials