Publication search
with myoelectric interface as keyword
Teng, Zhicheng Xu, Guanghua Zhang, Xun Chen, Xiaobi Zhang, Sicong Huang, Hsien-Yung
Published in
Journal of Neural Engineering
Objective. The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allo...
Zhao, Jiamin Yu, Yang Sheng, Xinjun Zhu, Xiangyang
Published in
Journal of Neural Engineering
Objective. Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining...
Sarasola-Sanz, Andrea López-Larraz, Eduardo Irastorza-Landa, Nerea Rossi, Giulia Figueiredo, Thiago McIntyre, Joseph Ramos-Murguialday, Ander
Published in
Frontiers in Neuroscience
Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional t...
Cheng, Larry Y. Che, Tiffanie Tomic, Goran Slutzky, Marc W. Paller, Ken A.
Published in
The Journal of Neuroscience
Memory reactivation during sleep reinforces various types of learning. Basic motor skills likely benefit from sleep. There is insufficient evidence, however, on whether memory reactivation during sleep contributes to learning how to execute a novel action. Here, we investigated motor learning in a myoelectric feedback task. Human male and female pa...
Liu, Gang Wang, Lu Wang, Jing
Published in
Journal of neural engineering
At present, sEMG-based gesture recognition requires vast amounts of training data; otherwise it is limited to a few gestures.Objective. This paper presents a novel dynamic energy model that decodes continuous hand actions by training small amounts of sEMG data.Approach. The activation of forearm muscles can set the corresponding fingers in motion o...
