Author name cluster

Xinjun Sheng

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

14 papers

2 author rows

JBHI Journal 2026 Journal Article

Simultaneous Decoding of Wrist Angles and Grasp Forces Based on Channel-Wise Cumulative Spike Trains

Yang Yu
Yang Xu
Jiamin Zhao
Dongxuan Li
Weichao Guo
Xinjun Sheng
Xiangyang Zhu

Understanding the underlying mechanism of neuromuscular system on motion/force generation is essential for human-machine interfacing. However, simultaneous decoding of wrist angles and grasp forces from neural signals remains an open challenge in the field of neural interfacing. In this study, we proposed a scheme leveraging channel-wise cumulative spike trains (cw-CSTs) of motor units to simultaneously decode wrist angles and grasp forces. Specifically, a spatial spike detection method was utilized to detect cw-CST from surface electromyography, observing as much as possible of motor unit activities. Accordingly, we extracted three neural features to drive the decoders, including a twitch force model-based (cw-MUdrive) and a discharge rate-based (DR-cwCST) neural features derived from cw-CSTs, and DR of motor units (DR-MUST) decomposed by a conventional blind source separation algorithm. Wrist- and hand-specific decoders were built to estimate wrist angles and grasp forces via Gaussian process regression. Experiments were conducted with ten subjects, in which they activated wrist motions and grasp forces concurrently. We evaluated the performance with both accuracy and output stability. Results demonstrated that the cwCST-based neural features outperformed the conventional DR-MUST features with both higher accuracy and stability metrics. Additionally, cw-MUdrive performed better than DR-cwCST in grasp force estimation and comparable to DR-cwCST in wrist angle estimation. The outcome provides an effective solution for simultaneously decoding wrist movements and hand grasp forces, promoting the development of natural control in neural interface.

Details DOI

IROS Conference 2025 Conference Paper

Hierarchical Reinforcement Learning for Articulated Tool Manipulation with Multifingered Hand

Wei Xu 0040
Yanchao Zhao
Weichao Guo
Xinjun Sheng

Manipulating articulated tools, such as tweezers or scissors, has rarely been explored in previous research. Unlike rigid tools, articulated tools change their shape dynamically, creating unique challenges for dexterous robotic hands. In this work, we present a hierarchical, goal-conditioned reinforcement learning (GCRL) framework to improve the manipulation capabilities of anthropomorphic robotic hands using articulated tools. Our framework comprises two policy layers: (1) a low-level policy that enables the dexterous hand to manipulate the tool into various configurations for objects of different sizes, and (2) a high-level policy that defines the tool’s goal state and controls the robotic arm for object-picking tasks. We employ an encoder, trained on synthetic pointclouds, to estimate the tool’s affordance states—specifically, how different tool configurations (e. g. , tweezer opening angles) enable grasping of objects of varying sizes—from input point clouds, thereby enabling precise tool manipulation. We also utilize a privilege-informed heuristic policy to generate replay buffer, improving the training efficiency of the high-level policy. We validate our approach through real-world experiments, showing that the robot can effectively manipulate a tweezer-like tool to grasp objects of diverse shapes and sizes with a 70. 8% success rate. This study highlights the potential of RL to advance dexterous robotic manipulation of articulated tools.

Details

JBHI Journal 2023 Journal Article

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

Yang Xu
Yang Yu
Miaojuan Xia
Xinjun Sheng
Xiangyang Zhu

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.

Details DOI

JBHI Journal 2023 Journal Article

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection

Yang Xu
Yang Yu
Zeming Zhao
Chen Chen
Xinjun Sheng

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 CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0. 96 $\pm$ 0. 03 and 10. 1% $\pm$ 3. 7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

Details DOI

YNIMG Journal 2022 Journal Article

Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings

Guangye Li
Shize Jiang
Jianjun Meng
Guohong Chai
Zehan Wu
Zhen Fan
Jie Hu
Xinjun Sheng

Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.

Details DOI

JBHI Journal 2022 Journal Article

Non-Invasive Analysis of Motor Unit Activation During Simultaneous and Continuous Wrist Movements

Chen Chen
Yang Yu
Xinjun Sheng
Xiangyang Zhu

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 techniques have become a prevailing solution to decode the motor neuron discharges from the spinal cord, whereas only single degree-of-freedom (DoF) movements are primarily involved in the current neural-based interfaces, resulting in limited intuitiveness and functionality. Here, we propose a non-invasive framework to analyze motor unit activities and estimate wrist torques during simultaneous contractions of multiple DoFs. Motor unit discharges were decoded from surface EMG signals and pooled into groups during sequential wrist movements. Then three neural features were extracted and linearly projected to the torques of multi-DoF tasks. On average, there were 44 $\pm$ 13 motor units identified for each motion with a PNR value of 25. 8 $\pm$ 2. 9 dB. The neural features outperformed the classic EMG feature on the estimation accuracy with higher correlation coefficients and smoothness. These results demonstrate the feasibility and superiority of the proposed framework in kinetics estimation of simultaneous movements, extending the potential applications of surface EMG decomposition in human-machine interfaces.

Details DOI

JBHI Journal 2021 Journal Article

EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding

Shihan Ma
Bo Lv
Chuang Lin
Xinjun Sheng
Xiangyang Zhu

Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error (RMSE), the improvement in signalto-noise ratio (SNRimp), and the percentage reduction in the correlation coefficient (η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18. 6, 19. 2, and 8. 0 dB for EMG signals corrupted with PLI, BW, and WGN at -6 dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20 dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.

Details DOI

JBHI Journal 2021 Journal Article

Wrist Torque Estimation via Electromyographic Motor Unit Decomposition and Image Reconstruction

Yang Yu
Chen Chen
Xinjun Sheng
Xiangyang Zhu

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 representations, which may lack local information around the discharge instant and ignore the subtle interactions of different MUs. In this study, we proposed an MU-specific image-based scheme for wrist torque estimation. Specifically, the high-density sEMG signals were decoded into motor unit spike trains (MUSTs), and then MU-specific images were reconstructed with MUSTs and corresponding motor unit action potential (MUAP). A convolutional neural network was used to learn representative features from MU-specific images automatically, and further to estimate wrist torques. The results demonstrated that the proposed method outperformed three conventional and a deep-learning regression approaches using DR features, with the estimation accuracy R 2 of 0. 82 ± 0. 09, 0. 89 ± 0. 06, and nRMSE of 12. 6 ± 2. 5%, 11. 0 ± 3. 1% for pronation/supination and flexion/extension, respectively. Further, the analysis of the extracted features from MU-specific images showed a higher correlation than DR for recorded torques, indicating the effectiveness of the proposed method. The outcomes of this study provide a novel and promising perspective for the intuitive control of neural interfacing.

Details DOI

JBHI Journal 2019 Journal Article

Electrode Density Affects the Robustness of Myoelectric Pattern Recognition System With and Without Electrode Shift

Jiayuan He
Xinjun Sheng
Xiangyang Zhu
Ning Jiang

With the availability of high-density (HD) electrodes technology, the electrodes used in myoelectric control can have much higher density than the current practice. In this study, we investigated the effects of electrode density on pattern recognition (PR) based myoelectric control. Four density levels were analyzed in two directions: parallel and perpendicular to muscle fibers. Their influence on PR-based myoelectric control algorithms was investigated under three conditions between training and testing datasets: no electrode shift, 10-mm shift parallel to muscle fibers and 10-mm shift perpendicular to muscle fibers. The effect of electrode density varied among the different shift conditions: First, when there was no shift, increasing electrode density significantly improved the classification performance; second, when the shift was in the perpendicular direction, increasing electrode density resulted in deterioration in the classification performance; third, when the shift was in the parallel direction, the effect of the electrode density was more complicated-increasing the density in the parallel direction reduced the performance, while increasing density in the perpendicular direction would initially enhance the performance, but then reduce performance. To our best knowledge, this was the first study focusing on the role of electrode density in myoelectric control with the presence of electrode shift. Its outcome would benefit the design of electrode placement for future myoelectric prostheses with HD electrodes.

Details DOI

JBHI Journal 2016 Journal Article

Reduced Daily Recalibration of Myoelectric Prosthesis Classifiers Based on Domain Adaptation

Jianwei Liu
Xinjun Sheng
Dingguo Zhang
Jiayuan He
Xiangyang Zhu

Control scheme design based on surface electromyography (sEMG) pattern recognition has been the focus of much research on a myoelectric prosthesis (MP) technology. Due to inherent nonstationarity in sEMG signals, prosthesis systems may need to be recalibrated day after day in daily use applications; thereby, hindering MP usability. In order to reduce the recalibration time in the subsequent days following the initial training, we propose a domain adaptation (DA) framework, which automatically reuses the models trained in earlier days as input for two baseline classifiers: a polynomial classifier (PC) and a linear discriminant analysis (LDA). Two novel algorithms of DA are introduced, one for PC and the other one for LDA. Five intact-limbed subjects and two transradial-amputee subjects participated in an experiment lasting ten days, to simulate the application of a MP over multiple days. The experiment results of four methods were compared: PC-DA (PC with DA), PC-BL (baseline PC), LDA-DA (LDA with DA), and LDA-BL (baseline LDA). In a new day, the DA methods reuse nine pretrained models, which were calibrated by 40 s training data per class in nine previous days. We show that the proposed DA methods significantly outperform nonadaptive baseline methods. The improvement in classification accuracy ranges from 5. 49% to 28. 48%, when the recording time per class is 2 s. For example, the average classification rates of PC-BL and PC-DA are 83. 70% and 92. 99%, respectively, for intact-limbed subjects with a nine-motions classification task. These results indicate that DA has the potential to improve the usability of MPs based on pattern recognition, by reducing the calibration time.

Details DOI

JBHI Journal 2014 Journal Article

Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface

Jianjun Meng
Xinjun Sheng
Dingguo Zhang
Xiangyang Zhu

One problem in the development of brain-computer interface (BCI) systems is to minimize the amount of subject training on the premise of accurate classification. Hence, the challenge is how to train the BCI system effectively especially in the scenario with small amount of training data. In this paper, we introduce improved semisupervised adaptation based on common spatial pattern (CSP) features. The feature extraction and classification are performed jointly and iteratively. In the iteration step, training data are expanded by part of the testing data with labels which are predicted by a linear discriminant analysis classifier and/or a Bayesian linear discriminant analysis classifier in the previous iteration. Then CSP features are reextracted from the expanded training data, and the classifiers are retrained. Both self-training and cotraining paradigms are proposed for the improved semisupervised adaptation. Throughout the investigation on different number of initial training trials, we find that when a small number of training trials are used, e. g. , a training session contains no more than 30 trials, similar classification performance to that of large training data items (40-50 trials) can be achieved. Effectiveness of the algorithms is verified by two competition datasets. Compared with several existing algorithms, the proposed semisupervised algorithms show improvements in classification accuracy for most of the competition datasets especially in the case of small training data.

Details DOI

JBHI Journal 2014 Journal Article

Invariant Surface EMG Feature Against Varying Contraction Level for Myoelectric Control Based on Muscle Coordination

Jiayuan He
Dingguo Zhang
Xinjun Sheng
Shunchong Li
Xiangyang Zhu

Variations in muscle contraction effort have a substantial impact on performance of pattern recognition based myoelectric control. Though incorporating changes into training phase could decrease the effect, the training time would be increased and the clinical viability would be limited. The modulation of force relies on the coordination of multiple muscles, which provides a possibility to classify motions with different forces without adding extra training samples. This study explores the property of muscle coordination in the frequency domain and found that the orientation of muscle activation pattern vector of the frequency band is similar for the same motion with different force levels. Two novel features based on discrete Fourier transform and muscle coordination were proposed subsequently, and the classification accuracy was increased by around 11% compared to the traditional time domain feature sets when classifying nine classes of motions with three different force levels. Further analysis found that both features decreased the difference among different forces of the same motion $p < 0. 005$ ) and maintained the distance among different motions $p > 0. 1$ ). This study also provided a potential way for simultaneous classification of hand motions and forces without training at all force levels.

Details DOI

ICRA Conference 2014 Conference Paper

Mechanical implementation of postural synergies using a simple continuum mechanism

Kai Xu 0001
Huan Liu 0009
Yuheng Du
Xinjun Sheng
Xiangyang Zhu

It is known that human controls muscles for hand poses in a coordinated manner and the coordination is referred to as a postural synergy. Using postural synergies, dexterous grasping tasks could be accomplished on a prosthetic hand via only a few (usually two) control inputs. Instead of implementing postural synergies digitally, this paper presents the design of a simple continuum mechanism for implementing the postural synergies mechanically. The design, fabrication and assembly of a prosthetic hand are firstly presented, followed by the synthesis of postural synergies from various grasping poses. Referring to the extracted postural synergies, structural parameters of the continuum mechanism are calculated based on a kinematics model. Experimental verifications are also presented to demonstrate the efficacy of the proposed idea.

Details

ICRA Conference 2011 Conference Paper

Time-stamped cross-coupled control in networked CNC systems

Xiong Xu
Xinjun Sheng
Zhenhua Xiong 0001
Xiangyang Zhu

This paper proposes a time-stamped cross-coupled control (TSCCC) algorithm to deal with the asynchronous sampling and network-induced delays in networked computer numerical control (CNC) machines. It uses time-stamps to estimate the network-induced delays from the sampling instants of different axes to the controller node. The network-induced delays are considered for accurately estimating the contour error in real-time. Furthermore, a networked CNC simulation system based on TrueTime toolbox is constructed, on which the proposed TSCCC algorithm is compared with the cross-coupled control (CCC) algorithm. Simulation results on two DC servomotors show that the TSCCC algorithm achieves better contour accuracy than the CCC algorithm.

Details