
Chen ChenShanghai Jiao Tong University | SJTU · Department of Mechanical Engineering
Chen Chen
Doctor of Engineering
About
41
Publications
5,676
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Introduction
My research focuses on EMG decomposition and human-machine interfacing. The motor neuron discharges are decoded from high-density surface EMG signals. The mapping methods are investigated between neural information and human kinematics and are used for the dexterous control of the prosthetic hand.
Skills and Expertise
Additional affiliations
June 2021 - present
June 2021 - present
Education
September 2015 - June 2021
September 2011 - June 2015
Publications
Publications (41)
Objective. The application of electromyography (EMG) decomposition techniques in myoelectric control has gradually increased. However, most decomposition-based control schemes rely on machine learning, lacking interpretation of the biological mechanisms underlying movement generation and requiring large datasets for training. As neuromusculoskeleta...
High-density electromyography (EMG) decomposition technology enables the myoelectric control on the level of individual motor units (MUs). Compared to traditional EMG-based control schemes, the MU-based control strategy exhibits enhanced reliability and superiority in the decoding of continuous motion and real-time control. Current MU-based control...
Muscles generate varying levels of force by recruiting different numbers of motor units (MUs), and as the force increases, the number of recruited MUs gradually rises. However, current decoding methods encounter difficulties in maintaining a stable and consistent growth trend in MU numbers with increasing force. In some instances, an unexpected red...
Electrical bioadhesive interfaces (EBIs) are standing out in various applications, including medical diagnostics, prosthetic devices, rehabilitation, and human-machine interactions. Nonetheless, crafting a reliable and advanced EBI with comprehensive properties spanning electrochemical, electrical, mechanical, and self-healing capabilities remains...
Surface Electromyographic (sEMG) signals contain motor-related information and therefore can be used for human-machine interaction (HMI). Deep learning plays an important role in extracting motor-related information from sEMG signals. However, most studies prioritize model accuracy without sufficient consideration of model efficiency, including the...
High‐density surface electromyography (sEMG) electrode arrays enable the recording of tens to hundreds of channels of electromyographic signals, which have found wide applications in clinics and human‐machine interfaces. However, current manufacturing technologies of high‐density sEMG electrode arrays generally involve high‐cost equipments, complic...
Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying C...
Development and implementation of neuroprosthetic hands is a multidisciplinary field at the interface between humans and artificial robotic systems, which aims at replacing the sensorimotor function of the upper-limb amputees as their own. Although prosthetic hand devices with myoelectric control can be dated back to more than 70 years ago, their a...
Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface elec...
A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and com...
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Surface electromyography (EMG) decomposition techniques have been developed to decode motor neuron activities non-invasively in the past decades, showing superior performance in human-machine interfaces such as gesture recognition and propor...
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. The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discr...
The adaptation of neural contractile properties has been observed in previous work. However, the neural changes on the motor unit (MU) level remain largely unknown. Voluntary movements are controlled through the precise activation of MU populations. In this work, we estimate the neural inputs from the spinal motor neurons to the muscles during isom...
Objective:
The surface electromyography (EMG) decomposition techniques have shown promising results in neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, current decomposition methods could only decode a limited number of motor units (MUs) because of the local convergence. The number of identified MUs rema...
Surface electromyography (EMG) signals have shown promising applications in human-machine interfacing (HMI) systems such as orthotics, prosthetics, and exoskeletons. Nevertheless, existing myoelectric control methods, generally based on time-domain or frequency-domain features, could not directly interpret neural commands. EMG decomposition techniq...
High-density surface electromyography (EMG) has been proposed to overcome the lower selectivity with respect to needle EMG and to provide information on a wide area over the considered muscle. Motor units decomposed from surface EMG signal of different depths differ in the distribution of action potentials detected in the skin surface. We propose a...
Objective:
Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific arrangements. However, the fiber orientation may change along the fiber paths and differ from...
Objective. Surface electromyography (EMG) decomposition techniques can be used to establish human-machine interfacing (HMI), but most investigations are implemented offline due to the computational load of the approach. Here, we generalize the offline decomposition algorithm to identify the motor unit (MU) activities in real time, and we propose a...
Simultaneous and proportional control (SPC) using surface electromyography (sEMG) signals has become a prevailing solution for the intuitive control of prosthesis and human-robot interaction. However, only time and frequency domain features are generally involved in conventional SPC algorithms, ignoring the globally spatial information across chann...
Neural interface using decomposed motor units (MUs) from surface electromyography (sEMG) has allowed non-invasive access to the neural control signals, and provided a novel approach for intuitive human-machine interaction. However, most of the existing methods based on decomposed MUs merely adopted the discharge rate (DR) as the feature representat...
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 ac...
Modeling of surface electromyographic (EMG) signal has been proven valuable for signal interpretation and algorithm validation. However, most EMG models are currently limited to single muscle, either with numerical or analytical approaches. Here, we present a preliminary study of a subject-specific EMG model with multiple muscles. Magnetic resonanc...
Objective:
Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG de...
Objective. The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand f...
Objective: Methods for surface electromyographic (EMG) signal decomposition have been developed in the past decade, to extract neural information transferred from the spinal cord to muscles. Here, we characterize the accuracy in the identification of motor unit activities during hand postures from highdensity EMG signals and we propose amapping app...
The aim of the study was to apply the real-time surface electromyography (EMG) decomposition to the continuous estimation of grasp kinematics. A real-time decomposition scheme based on the convolutional compensation kernel algorithm was proposed. High-density surface EMG signals and grasp kinematics were recorded concurrently from five able-bodied...
Motor unit (MU) global firing rate is widely applied in physiological and clinical investigation. Currently it still remains difficult to measure the MU global firing rate from sEMG. In this study, we propose a new feature of maximum power amplitude (MPA) from sEMG power spectrum. Based on an analysis of mathematical model and simulated signals, MP...
Objective. The aim of the study was to characterize the accuracy in the identification of motor unit discharges during natural movements using high-density electromyography (EMG) signals and to investigate their correlation with finger kinematics. Approach. High-density EMG signals of forearm muscles and finger joint angles were recorded concurrent...