Chen Chen
Shanghai Jiao Tong University
17 Papers
5 Citations
Chen Chen is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Motor unit. The author has an hindex of 5, co-authored 17 publications.
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Papers
Hand gesture recognition based on motor unit spike trains decoded from high-density electromyography
TL;DR: The accuracy in the identification of motor unit activities during hand postures from high-density EMG signals is characterized and a mapping approach between these neural signals and hand gestures is proposed and demonstrates high classification accuracy of the hand gestures.
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Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals
TL;DR: The results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.
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Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time.
TL;DR: In this paper, a motor unit (MU) activity was identified in real-time using surface electromyography (EMG) signals from forearm muscles and the motor unit spike trains (MUSTs) were extracted as the control signal for each motor task.
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iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes
Guangye Li,Shize Jiang,Chen Chen,Peter Brunner,Peter Brunner,Zehan Wu,Gerwin Schalk,Gerwin Schalk,Liang Chen,Dingguo Zhang +9 more
TL;DR: iEEGview is the first public Matlab GUI based software for intracranial electrode localization and visualization that hold integrated capabilities together within one pipeline and can serve as a useful tool in facilitating iEEG studies.
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Multi-DoF continuous estimation for wrist torques using stacked autoencoder
TL;DR: A stacked autoencoder-based deep neural network is constructed to continuously estimate multiple degrees-of-freedom (DoFs) kinetics of wrist from sEMG signals to demonstrate the feasibility of this scheme and significant superiority of SAE-DNN over LR and SVR with higher R 2 values across all DoFs.
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