1. Analysis of motor unit activities during multiple motor tasks by real-time EMG decomposition: perspective for myoelectric control
- Author
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Yang Yu, Xiangyang Zhu, Chen Chen, and Xinjun Sheng
- Subjects
medicine.diagnostic_test ,Electromyography ,Computer science ,business.industry ,0206 medical engineering ,Proportional control ,Pattern recognition ,02 engineering and technology ,020601 biomedical engineering ,Signal ,Motor unit ,Forearm ,Motion ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Decomposition (computer science) ,Humans ,Artificial intelligence ,Muscle, Skeletal ,business ,Algorithms ,030217 neurology & neurosurgery ,Neural decoding - Abstract
Surface electromyography (EMG) decomposition techniques have been applied for human-machine interfacing by decoding neural information, while most of decomposition approaches work offline. Here, we apply an online decomposition scheme to decode motor unit activities during three motor tasks, and measure the recognition accuracy of motor type and activation level using the decomposition results. High-density surface EMG signal were recorded from forearm muscles of six able-bodied subjects. The EMG signals were decomposed into motor unit spike trains (MUST) with a sliding window of 100 ms. The computation complexity had time consumption50 ms in each window. Most identified motor units discharged during only one motor task. On average, over 5 MUSTs were identified for each motion and the recognition accuracy based on motor unit activities was99%. The discharge rate of motor units was highly correlated with the activation level of each motion with an average correlation coefficient of 0.94 ± 0.04. These results indicate the feasibility of an online, multi-motion, and proportional control scheme based on neural decoding in a non-invasive way.
- Published
- 2020