@article{liu_naseri_lee_hu_lewek_huang_2023, title={A simplified model for whole-body angular momentum calculation}, volume={111}, ISSN={["1873-4030"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85144824437&partnerID=MN8TOARS}, DOI={10.1016/j.medengphy.2022.103944}, abstractNote={The capability to monitor gait stability during everyday life could provide key information to guide clinical intervention to patients with lower limb disabilities. Whole body angular momentum (Lbody) is a convenient stability indicator for wearable motion capture systems. However, Lbody is costly to estimate, because it requires monitoring all major body segment using expensive sensor elements. In this study, we developed a simplified rigid body model by merging connected body segments to reduce the number of body segments, which need to be monitored. We demonstrated that the Lbody could be estimated by a seven-segment model accurately for both people with and without lower extremity amputation.}, journal={MEDICAL ENGINEERING & PHYSICS}, author={Liu, Ming and Naseri, Amirreza and Lee, I-Chieh and Hu, Xiaogang and Lewek, Michael D. and Huang, He}, year={2023}, month={Jan} }
 @article{rubin_hinson_saul_hu_huang_2023, title={Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation}, volume={31}, ISSN={["1558-0210"]}, url={http://dx.doi.org/10.1109/tnsre.2023.3336543}, DOI={10.1109/TNSRE.2023.3336543}, abstractNote={There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) MU firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered prostheses.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Rubin, Noah and Hinson, Robert and Saul, Katherine and Hu, Xiaogang and Huang, He}, year={2023}, pages={4821–4830} }
 @article{roy_zheng_kamper_hu_2023, title={Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities}, volume={70}, ISSN={["1558-2531"]}, DOI={10.1109/TBME.2022.3232067}, abstractNote={Objective: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. Methods: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. Results: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. Conclusion: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Significance: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.}, number={6}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Roy, Rinku and Zheng, Yang and Kamper, Derek G. G. and Hu, Xiaogang}, year={2023}, month={Jun}, pages={1911–1920} }
 @article{hinson_berman_filer_kamper_hu_huang_2023, title={Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-Time Operation of Electromyography-Based Neural-Machine Interfaces}, volume={31}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85144812628&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2022.3226229}, abstractNote={There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R2 ≈ 0.4), moderate (R2 ≈ 0.6), and high ( $\text {R}^{\vphantom {\text {D}^{\text {a}}}{2}} \approx 0.8$ ) offline performance levels. Twelve non-disabled subjects trained with each offline performance level decoder before evaluating final real-time posture matching performance. Moderate to strong relationships were detected between offline performance and all real-time task performance metrics: task completion percentage (r = 0.66, p < 0.001), normalized task completion time (r = −0.51, p = 0.001), path efficiency (r = 0.74, p < 0.001), and target overshoots (r = −0.79, p < 0.001). Significant improvements in each real-time task evaluation metric were also observed between the different offline performance levels. Additionally, subjects rated myoelectric controllers with higher offline performance more favorably. The results of this study support the use and validity of offline analyses for optimization of NMIs in myoelectric control research and development.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Hinson, Robert M. and Berman, Joseph and Filer, William and Kamper, Derek and Hu, Xiaogang and Huang, He}, year={2023}, pages={680–689} }
 @article{roy_kamper_hu_2023, title={Optimized Model Selection for Concurrent Decoding of Finger Kinetics and Kinematics}, volume={11}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2023.3246950}, abstractNote={Myoelectric-based motor intent detection is typically used to interface with assistive devices. However, the intent detection performance is sensitive to interference of electromyogram (EMG) signals. Recently, EMG signals are decomposed into motor units (MU) firing activities, and neuron binary firing activities can be used to predict motor output in a continuous manner. Different functions that map MU firings to motor output have been implemented, and both composite MU firing frequency and individual MU firing frequency have been used. It is unclear whether one mapping function outperform others. Accordingly, we evaluated three MU-based finger kinetic and kinematic prediction models, by varying the number of MUs and the method of including MU firings into the regression model. We also compared the performance of three EMG amplitude-based models with varying number of channels. We performed MU decomposition in advance for real-time implementations. Our results showed that individual firing frequency of five MUs provided the lowest estimation error (force: 4.66±0.36 %MVC; joint angle: 4.81±0.49°) and highest correlation (force: 0.86±0.01; joint angle: 0.93±0.01) with the measured motor outputs, when compared with mapping method using the populational firing frequency of all MUs or the populational firing frequency of a group of MUs with similar firing activities. The results indicated that firing information at the population level may mask critical information of individual MU firings. These findings allowed us to identify the optimal models for concurrent and continuous finger force and joint angle estimation. A combination of the minimal level of complexity and high accuracy make these models suitable for real-time control of assistive robotic devices.}, journal={IEEE ACCESS}, author={Roy, Rinku and Kamper, Derek G. G. and Hu, Xiaogang}, year={2023}, pages={17348–17358} }
 @article{roy_xu_kamper_hu_2022, title={A generic neural network model to estimate populational neural activity for robust neural decoding}, volume={144}, ISSN={["1879-0534"]}, DOI={10.1016/j.compbiomed.2022.105359}, abstractNote={Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational motoneuron firing activities. We implemented convolutional neural networks (CNNs) to learn the mapping from high-density electromyogram (HD-EMG) signals of forearm muscles to populational motoneuron firing frequency. We first extracted the spatiotemporal features of EMG energy and frequency maps to improve learning efficiency, given that EMG signals are intrinsically stochastic. We then established a generic neural network model by training on the populational neuron firing activities of multiple participants. Using a regression model, we continuously predicted individual finger forces in real-time. We compared the force prediction performance with two state-of-the-art approaches: a neuron-decomposition method and a classic EMG-amplitude method. Our results showed that the generic CNN model outperformed the subject-specific neuron-decomposition method and the EMG-amplitude method, as demonstrated by a higher correlation coefficient between the measured and predicted forces, and a lower force prediction error. In addition, the CNN model revealed more stable force prediction performance over time. Overall, our approach provides a generic and efficient continuous neural decoding approach for real-time and robust human-robot interactions.}, journal={COMPUTERS IN BIOLOGY AND MEDICINE}, author={Roy, Rinku and Xu, Feng and Kamper, Derek G. and Hu, Xiaogang}, year={2022}, month={May} }
 @article{rubin_zheng_huang_hu_2022, title={Finger Force Estimation Using Motor Unit Discharges Across Forearm Postures}, volume={69}, ISSN={["1558-2531"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85125331087&partnerID=MN8TOARS}, DOI={10.1109/TBME.2022.3153448}, abstractNote={Background: Myoelectric- based decoding has gained popularity in upper- limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. Methods: We extracted MU information from high- density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects’ maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. Results: We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64% MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36% MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC). Conclusion: Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.}, number={9}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Rubin, Noah and Zheng, Yang and Huang, He and Hu, Xiaogang}, year={2022}, month={Sep}, pages={2767–2775} }
 @article{vargas_huang_zhu_hu_2022, title={Object Recognition via Evoked Sensory Feedback during Control of a Prosthetic Hand}, volume={7}, ISSN={["2377-3766"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85118588970&partnerID=MN8TOARS}, DOI={10.1109/LRA.2021.3122897}, abstractNote={Haptic and proprioceptive feedback is critical for sensorimotor integration when we use our hand to perform daily tasks. Here, we evaluated how externally evoked haptic and proprioceptive feedback and myoelectric control strategies affected the recognition of object properties when participants controlled a prosthetic hand. Fingertip haptic sensation was elicited using a transcutaneous nerve stimulation grid to encode the prosthetic's fingertip forces. An array of tactors elicited patterned vibratory stimuli to encode tactile-proprioceptive kinematic information of the prosthetic finger joint. Myoelectric signals of the finger flexor and extensor were used to control the position or velocity of joint angles of the prosthesis. Participants were asked to perform object property (stiffness and size) recognition, by controlling the prosthetic hand with concurrent haptic and tactile-proprioceptive feedback. With the evoked feedback, intact and amputee participants recognized the object stiffness and size at success rates ranging from 50% to 100% in both position and velocity control with no significant difference across control schemes. Our findings show that evoked somatosensory feedback in a non-invasive manner can facilitate closed-loop control of the prosthetic hand and allowed for simultaneous recognition of different object properties. The outcomes can facilitate our understanding on the role of sensory feedback during bidirectional human-machine interactions, which can potentially promote user experience in object interactions using prosthetic hands.}, number={1}, journal={IEEE ROBOTICS AND AUTOMATION LETTERS}, author={Vargas, Luis and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2022}, month={Jan}, pages={207–214} }
 @article{vargas_huang_zhu_kamper_hu_2022, title={Resembled Tactile Feedback for Object Recognition Using a Prosthetic Hand}, volume={7}, ISSN={["2377-3766"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85136090957&partnerID=MN8TOARS}, DOI={10.1109/LRA.2022.3196958}, abstractNote={Tactile feedback in the hand is essential for interaction with objects. Here, we evaluated how artificial tactile sensation affected the recognition of object properties using a myoelectrically controlled prosthetic hand. Electromyogram signals from the flexor and extensor finger muscles were used to continuously control either prosthetic joint velocity or position. Participants grasped objects of varying shape or size using the prosthetic hand. Tactile feedback was evoked by transcutaneous nerve stimulation along the participant's upper arm and modulated based on the prosthetic-object contact force. Multi-channel electrical stimulation targeted the median and ulnar nerve bundles to produce resembled tactile sensations at distinct hand regions. The results showed that participants could gauge the onset timing of tactile feedback to discern object shape and size. We also found that the position-controller led to a greater recognition accuracy of object size compared with velocity-control, potentially due to supplemental joint position information from muscle activation level. Our findings demonstrate that non-invasive tactile feedback can enable effective object shape and size recognition during prosthetic control. The evaluation of tactile feedback across myoelectric controllers can help understand the interplay between sensory and motor pathways involved in the control of assistive devices.}, number={4}, journal={IEEE ROBOTICS AND AUTOMATION LETTERS}, author={Vargas, Luis and Huang, He and Zhu, Yong and Kamper, Derek and Hu, Xiaogang}, year={2022}, month={Oct}, pages={10977–10984} }
 @article{lee_liu_lewek_hu_filer_huang_2022, title={Toward Safe Wearer-Prosthesis Interaction: Evaluation of Gait Stability and Human Compensation Strategy Under Faults in Robotic Transfemoral Prostheses}, volume={30}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85139401676&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2022.3208778}, abstractNote={Although advanced wearable robots can assist human wearers, their internal faults (i.e., sensors or control errors) also pose a challenge. To ensure safe wearer-robot interactions, how internal errors by the prosthesis limb affect the stability of the user-prosthesis system, and how users react and compensate for the instability elicited by internal errors are imperative. The goals of this study were to 1) systematically investigate the biomechanics of a wearer-robot system reacting to internal errors induced by a powered knee prosthesis (PKP), and 2) quantify the error tolerable bound that does not affect the user’s gait stability. Eight non-disabled participants and two unilateral transfemoral amputees walked on a pathway wearing a PKP, as the controller randomly switched the control parameters to disturbance parameters to mimic the errors caused by locomotion mode misrecognition. The size of prosthesis control errors was systematically varied to determine the error tolerable bound that disrupted gait stability. The effect of the error was quantified based on the 1) mechanical change described by the angular impulse applied by the PKP, and 2) overall gait instability quantified using human perception, angular momentum, and compensatory stepping. The results showed that the error tolerable bound is dependent on the gait phase and the direction of torque change. Two balance recovery strategies were also observed to allow participants to successful respond to the induced errors. The outcomes of this study may assist the future design of an auto-tuning algorithm, volitionally-controlled powered prosthetic legs, and training of gait stability.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Lee, I-Chieh and Liu, Ming and Lewek, Michael D. and Hu, Xiaogang and Filer, William G. and Huang, He}, year={2022}, pages={2773–2782} }
 @article{yao_zhou_hinson_dong_wu_ives_hu_huang_zhu_2022, title={Ultrasoft Porous 3D Conductive Dry Electrodes for Electrophysiological Sensing and Myoelectric Control}, volume={5}, ISSN={["2365-709X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85132598682&partnerID=MN8TOARS}, DOI={10.1002/admt.202101637}, abstractNote={Abstract}, number={10}, journal={ADVANCED MATERIALS TECHNOLOGIES}, author={Yao, Shanshan and Zhou, Weixin and Hinson, Robert and Dong, Penghao and Wu, Shuang and Ives, Jasmine and Hu, Xiaogang and Huang, He and Zhu, Yong}, year={2022}, month={May} }
 @article{shin_hawari_hu_2021, title={Activation of Superficial and Deep Finger Flexors Through Transcutaneous Nerve Stimulation}, volume={25}, ISSN={["2168-2208"]}, DOI={10.1109/JBHI.2020.3041669}, abstractNote={Objective: Functional electrical stimulation (FES) is a common technique to elicit muscle contraction and help improve muscle strength. Traditional FES over the muscle belly typically only activates superficial muscle regions. In the case of hand FES, this prevents the activation of the deeper flexor muscles which control the distal finger joints. Here, we evaluated whether an alternative transcutaneous nerve-bundle stimulation approach can activate both superficial and deep extrinsic finger flexors using a high-density stimulation grid. Methods: Transverse ultrasound of the forearm muscles was used to obtain cross-sectional images of the underlying finger flexors during stimulated finger flexions and kinematically-matched voluntary motions. Finger kinematics were recorded, and an image registration method was used to capture the large deformation of the muscle regions during each flexion. This deformation was used as a surrogate measure of the contraction of muscle tissue, and the regions of expanding tissue can identify activated muscles. Results: The nerve-bundle stimulation elicited contractions in the superficial and deep finger flexors. Both separate and concurrent activation of these two muscles were observed. Joint kinematics of the fingers also matched the expected regions of muscle contractions. Conclusions: Our results showed that the nerve-bundle stimulation technique can activate the deep extrinsic finger flexors, which are typically not accessible via traditional surface FES. Significance: Our nerve-bundle stimulation method enables us to produce the full range of motion of different joints necessary for various functional grasps, which could benefit future neuroprosthetic applications.}, number={7}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Shin, Henry and Hawari, Marwan A. and Hu, Xiaogang}, year={2021}, month={Jul}, pages={2575–2582} }
 @article{zheng_shin_kamper_hu_2021, title={Automatic Detection of Contracting Muscle Regions via the Deformation Field of Transverse Ultrasound Images: A Feasibility Study}, volume={49}, ISSN={["1573-9686"]}, DOI={10.1007/s10439-020-02557-2}, abstractNote={Accurate identification of contracting muscles can help us to understand the muscle function in both physiological and pathological conditions. Conventional electromyography (EMG) have limited access to deep muscles, crosstalk, or instability in the recordings. Accordingly, a novel framework was developed to detect contracting muscle regions based on the deformation field of transverse ultrasound images. We first estimated the muscle movements in a stepwise calculation, to derive the deformation field. We then calculated the divergence of the deformation field to locate the expanding or shrinking regions during muscle contractions. Two preliminary experiments were performed to evaluate the feasibility of the developed algorithm. Using concurrent intramuscular EMG recordings, Experiment I verified that the divergence map can capture the activity of superficial and deep muscles, when muscles were activated voluntarily or through electrical stimulation. Experiment II verified that the divergence map can only capture contracting muscles but not muscle shortening during passive movements. The results demonstrated that the divergence can individually capture the activity of muscles at different depths, and was not sensitive to muscle shortening during passive movements. The proposed framework can automatically detect the regions of contracting muscle, and could potentially serve as a tool to assess the functions of a group of muscles concurrently.}, number={1}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Zheng, Yang and Shin, Henry and Kamper, Derek G. and Hu, Xiaogang}, year={2021}, month={Jan}, pages={354–366} }
 @article{vargas_huang_zhu_hu_2021, title={Closed-loop control of a prosthetic finger via evoked proprioceptive information}, volume={18}, ISSN={["1741-2552"]}, url={http://dx.doi.org/10.1088/1741-2552/ac3c9e}, DOI={10.1088/1741-2552/ac3c9e}, abstractNote={Abstract}, number={6}, journal={JOURNAL OF NEURAL ENGINEERING}, publisher={IOP Publishing}, author={Vargas, Luis and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2021}, month={Dec} }
 @article{rubin_liu_hu_huang_2021, title={Common Neural Input within and across Lower Limb Muscles: A Preliminary Study}, ISSN={["1558-4615"]}, url={http://dx.doi.org/10.1109/embc46164.2021.9630141}, DOI={10.1109/EMBC46164.2021.9630141}, abstractNote={Motor units (MUs) are the basic unit of motor control. MU synchronization has been evaluated to identify common inputs in neural circuitry during motor coordination. Recent studies have compared common inputs between muscles in the lower limb, but further investigation is needed to compare common inputs to MUs both within a muscle and between MUs of different muscle pairs. The goal of this preliminary study was to characterize levels of common inputs to MUs in three muscle groups: MUs within a muscle, between bilateral homologous pairs, and between agonist/antagonist muscle pairs. To achieve this, surface electromyography (EMG) was recorded during bilateral ankle dorsiflexion and plantarflexion on the right and left tibiales anterior (RTA, LTA) and gastrocnemii (RGA, LGA) muscles. After decomposing EMG into active MU firings, we conducted coherence analyses of composite MU spike trains (CSTs) in each muscle group in both the beta (13-30 Hz) and gamma (30-60 Hz) frequency bands. Our results indicate MUs within a muscle have the greatest levels of common input, with decreasing levels of common input to bilateral and agonist/antagonist muscle pairs, respectively. Additionally, each muscle group exhibited similar levels of common input between the beta and gamma bands. This work may provide a way to unveil mechanisms of functional coordination in the lower limb across motor tasks.}, journal={2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)}, publisher={IEEE}, author={Rubin, Noah and Liu, Wentao and Hu, Xiaogang and Huang, He}, year={2021}, pages={6683–6686} }
 @article{zheng_hu_2021, title={Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information}, volume={31}, ISSN={["1793-6462"]}, DOI={10.1142/S0129065721500106}, abstractNote={ A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner. }, number={06}, journal={INTERNATIONAL JOURNAL OF NEURAL SYSTEMS}, author={Zheng, Yang and Hu, Xiaogang}, year={2021}, month={Jun} }
 @article{xu_zheng_hu_2021, title={Estimation of Joint Kinematics and Fingertip Forces using Motoneuron Firing Activities: A Preliminary Report}, ISSN={["1948-3546"]}, DOI={10.1109/NER49283.2021.9441433}, abstractNote={A loss of individuated finger movement affects critical aspects of daily activities. There is a need to develop neural-machine interface techniques that can continuously decode single finger movements. In this preliminary study, we evaluated a novel decoding method that used finger-specific motoneuron firing frequency to estimate joint kinematics and fingertip forces. High-density electromyogram (EMG) signals were obtained during which index or middle fingers produced either dynamic flexion movements or isometric flexion forces. A source separation method was used to extract motor unit (MU) firing activities from a single trial. A separate validation trial was used to only retain the MUs associated with a particular finger. The finger-specific MU firing activities were then used to estimate individual finger joint angles and isometric forces in a third trial using a regression method. Our results showed that the MU firing based approach led to smaller prediction errors for both joint angles and forces compared with the conventional EMG amplitude based method. The outcomes can help develop intuitive neural-machine interface techniques that allow continuous single-finger level control of robotic hands. In addition, the previously obtained MU separation information was applied directly to new data, and it is therefore possible to enable online extraction of MU firing activities for real-time neural-machine interactions.}, journal={2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)}, author={Xu, Feng and Zheng, Yang and Hu, Xiaogang}, year={2021}, pages={1035–1038} }
 @misc{fleming_stafford_huang_hu_ferris_huang_2021, title={Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions}, volume={18}, ISSN={["1741-2552"]}, url={http://dx.doi.org/10.1088/1741-2552/ac1176}, DOI={10.1088/1741-2552/ac1176}, abstractNote={Abstract}, number={4}, journal={JOURNAL OF NEURAL ENGINEERING}, publisher={IOP Publishing}, author={Fleming, Aaron and Stafford, Nicole and Huang, Stephanie and Hu, Xiaogang and Ferris, Daniel P. and Huang, He}, year={2021}, month={Aug} }
 @article{vargas_huang_zhu_hu_2021, title={Perception of Static Position and Kinesthesia of the Finger using Vibratory Stimulation}, volume={2021-May}, ISSN={["1948-3546"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85107464376&partnerID=MN8TOARS}, DOI={10.1109/NER49283.2021.9441255}, abstractNote={Proprioception provides information regarding the state of an individual's limb in terms of static position and kinesthesia (dynamic movement). When such feedback is lost or impaired, the performance of dexterous control of our biological limbs or assistive devices tends to deteriorate. In this study, we determined if external vibratory stimulation patterns could allow for the perception of a finger's static position and kinesthesia. Using four tactors and two stimulus levels, eight vibratory settings corresponded to eight discrete finger positions. The transition patterns between these eight settings corresponded to kinesthesia. Three experimental blocks assessed the perception of a finger's static position, speed, and movement (amplitude and direction). Our results demonstrated that both position and kinesthesia could be recognized with over 93% accuracy. The outcomes suggest that vibratory stimulus can inform subjects of static and dynamic aspects of finger proprioception. This sensory stimulation approach can be implemented to improve outcomes in clinical populations with sensory deficits, and to enhance user experience when users interact with assistive devices.}, journal={2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)}, author={Vargas, Luis and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2021}, pages={1087–1090} }
 @article{vargas_huang_zhu_hu_2021, title={Static and dynamic proprioceptive recognition through vibrotactile stimulation}, volume={18}, ISSN={["1741-2552"]}, url={http://dx.doi.org/10.1088/1741-2552/ac0d43}, DOI={10.1088/1741-2552/ac0d43}, abstractNote={Objective. Proprioceptive information provides individuals with a sense of our limb’s static position and dynamic movement. Impaired or a lack of such feedback can diminish our ability to perform dexterous motions with our biological limbs or assistive devices. Here we seek to determine whether both static and dynamic components of proprioception can be recognized using variation of the spatial and temporal components of vibrotactile feedback. Approach. An array of five vibrotactors was placed on the forearm of each subject. Each tactor was encoded to represent one of the five forearm postures. Vibratory stimulus was elicited to convey the static position and movement of the forearm. Four experimental blocks were performed to test each subject’s recognition of a forearm’s simulated static position, rotational amplitude, rotational amplitude and direction, and rotational speed. Main results. Our results showed that the subjects were able to perform proprioceptive recognition based on the delivered vibrotactile information. Specifically, rotational amplitude recognition resulted in the highest level of accuracy (99.0%), while the recognition accuracy of the static position and the rotational amplitude-direction was the lowest (91.7% and 90.8%, respectively). Nevertheless, all proprioceptive properties were perceived with >90% accuracy, indicating that the implemented vibrotactile encoding scheme could effectively provide proprioceptive information to the users. Significance. The outcomes suggest that information pertaining to static and dynamic aspects of proprioception can be accurately delivered using an array of vibrotactors. This feedback approach could be used to potentially evaluate the sensorimotor integration processes during human–machine interactions, and to improve sensory feedback in clinical populations with somatosensory impairments.}, number={4}, journal={JOURNAL OF NEURAL ENGINEERING}, publisher={IOP Publishing}, author={Vargas, Luis and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2021}, month={Aug} }
 @article{xie_hu_2021, title={Virtual Reality for Evaluating Prosthetic Hand Control Strategies: A Preliminary Report}, ISSN={["1558-4615"]}, DOI={10.1109/EMBC46164.2021.9630555}, abstractNote={Improving prosthetic hand functionality is critical in reducing abandonment rates and improving the amputee’s quality of life. Techniques such as joint force estimation and gesture recognition using myoelectric signals could enable more realistic control of the prosthetic hand. To accelerate the translation of these advanced control strategies from lab to clinic, We created a virtual prosthetic control environment that enables rich user interactions and dexterity evaluation. The virtual environment is made of two parts, namely the Unity scene for rendering and user interaction, and a Python back-end to support accurate physics simulation and communication with control algorithms. By utilizing the built-in tracking capabilities of a virtual reality headset, the user can visualize and manipulate a virtual hand without additional motion tracking setups. In the virtual environment, we demonstrate actuation of the prosthetic hand through decoded EMG signal streaming, hand tracking, and the use of a VR controller. By providing a flexible platform to investigate different control modalities, we believe that our virtual environment will allow for faster experimentation and further progress in clinical translation.}, journal={2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)}, author={Xie, Jason and Hu, Xiaogang}, year={2021}, pages={6263–6266} }
 @article{wu_yao_liu_hu_huang_zhu_2020, title={Buckle-Delamination-Enabled Stretchable Silver Nanowire Conductors}, volume={12}, ISSN={["1944-8252"]}, url={http://dx.doi.org/10.1021/acsami.0c09775}, DOI={10.1021/acsami.0c09775}, abstractNote={Controlled buckling and delamination of thin films on a compliant substrate has attracted much attention for applications ranging from micro/nanofabrication to flexible and stretchable electronics to bioengineering. Here a highly conductive and stretchable conductor is fabricated by attaching a polymer composite film (with a thin layer of silver nanowires embedded below the surface of the polymer matrix) on top of a pre-stretched elastomer substrate followed with releasing the prestrain. A partially delaminated wavy geometry of the polymer film is created. During the evolution of the buckle delamination, the blisters pop up randomly but self-adjust into a uniform distribution, which effectively reduces the local strain in the silver nanowires. The resistance change of the conductor is less than 3% with the applied strain up to 100%. A theoretical model on the buckle-delamination structure is developed to predict the geometrical evolution, which agrees well with experimental observation. Finally, an integrated silver nanowire/elastomer sensing module and a stretchable thermochromic device are developed to demonstrate the utility of the stretchable conductor. This work highlights the important relevance of mechanics-based design in nanomaterial-enabled stretchable devices.}, number={37}, journal={ACS APPLIED MATERIALS & INTERFACES}, publisher={American Chemical Society (ACS)}, author={Wu, Shuang and Yao, Shanshan and Liu, Yuxuan and Hu, Xiaogang and Huang, He Helen and Zhu, Yong}, year={2020}, month={Sep}, pages={41696–41703} }
 @article{zheng_hu_2020, title={Elicited upper limb motions through transcutaneous cervical spinal cord stimulation}, volume={17}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/ab8f6f}, abstractNote={Objective. Transcutaneous cervical spinal cord stimulation (tsCSC) has been demonstrated to activate the dorsal root and activate targeted muscles. However, it is unclear whether tsCSC can elicit functionally relevant movements of the upper limb for assistive/rehabilitative purposes. Approach. The current study sought to elicit arm and hand movements by tsCSC by placing an electrode array near the cervical segments of the spinal cord. Anode stimulation current pulses were delivered to the dorsal side at 120 Hz and 30 Hz in separate trials. The elicited joint kinematics were captured using a motion tracking system. Main results. The results revealed that distal and proximal joint movements can be elicited either independently or synergistically. Specifically, different motions, including flexion and extension of the elbow, wrist, and five digits, can be selectively elicited by adjusting the stimulation parameters, such as stimulation location and stimulation intensity. Significance. The findings demonstrated the feasibility of the spinal cord stimulation technique in eliciting functional movements of the upper limb. The outcomes also revealed the potential of the tsCSC technique as a promising assistive or rehabilitative method for individuals with impaired function of the upper limb.}, number={3}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2020}, month={Jun} }
 @article{dai_hu_2020, title={Finger Joint Angle Estimation Based on Motoneuron Discharge Activities}, volume={24}, ISSN={["2168-2208"]}, DOI={10.1109/JBHI.2019.2926307}, abstractNote={Estimation of joint kinematics plays an important role in intuitive human–machine interactions. However, continuous and reliable estimation of small (e.g., the finger) joint angles is still a challenge. The objective of this study was to continuously estimate finger joint angles using populational motoneuron firing activities. Multi-channel surface electromyogram (sEMG) signals were obtained from the extensor digitorum communis muscles, while the subjects performed individual finger oscillatory extension movements at two different speeds. The individual finger movement was first classified based on the EMG signals. The discharge timings of individual motor units were extracted through high-density EMG decomposition, and were then pooled as a composite discharge train. The firing frequency of the populational motor unit firing events was used to represent the descending neural drive to the motor unit pool. A second-order polynomial regression was then performed to predict the measured metacarpophalangeal extension angle using the derived neural drive based on the neuronal firings. Our results showed that individual finger extension movement can be classified with >96% accuracy based on multi-channel EMG. The extension angles of individual fingers can be predicted continuously by the derived neural drive with R2 values >0.8. The performance of the neural-drive-based approach was superior to the conventional EMG-amplitude-based approach, especially during fast movements. These findings indicated that the neural-drive-based interface was a promising approach to reliably predict individual finger kinematics.}, number={3}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Dai, Chenyun and Hu, Xiaogang}, year={2020}, month={Mar}, pages={760–767} }
 @article{zheng_hu_2020, title={Muscle activation pattern elicited through transcutaneous stimulation near the cervical spinal cord}, volume={17}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/ab5e09}, abstractNote={Objective. Neuromuscular electrical stimulation can help activate muscles of individuals with neurological disorders. However, conventional electrical stimulation targets distal branches of motor axons, and activates muscles non-physiologically. For example, stimulation at the muscle belly activates muscles in a highly synchronized manner. Accordingly, we investigated whether the muscle activation pattern was more asynchronized through transcutaneous stimulation near the cervical spinal cord (tsCSC). Approach. A stimulation array was placed on the posterior side near the cervical spinal cord, to target the arm and hand muscles. Stimulation trains of 10 Hz and 30 Hz were delivered. Electromyogram signals were recorded to quantify the muscle activation patterns. Arm and finger joint kinematics were also recorded using a motion capture system. Main results. After an initial synchronized activation prior to 35 ms after stimulation onset, we observed substantial asynchronized muscle activities with a long latency (>35 ms). The asynchronized activation is also more evident in distal muscles compared with the proximal muscles. In addition, the decreased synchronization level of muscle activities correlated with a reduced fluctuation of joint movement. The highly asynchronized muscle activities indicated an activation of the sensory axons and/or dorsal roots as well as a possible involvement of some spinal-supraspinal circuitry. Significance. Our tsCSC approach can improve the muscle activation pattern during electrical stimulation with a possible involvement of the spinal and supraspinal pathways, which can facilitate applications on rehabilitation/assistance of individuals with impaired motor function.}, number={1}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2020}, month={Feb} }
 @article{vargas_huang_zhu_hu_2020, title={Object Shape and Surface Topology Recognition Using Tactile Feedback Evoked through Transcutaneous Nerve Stimulation}, volume={13}, ISSN={["2329-4051"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85078214377&partnerID=MN8TOARS}, DOI={10.1109/TOH.2020.2967366}, abstractNote={Tactile feedback is critical for distinguishing different object properties. In this article, we determined if tactile feedback evoked by transcutaneous nerve stimulation can be used to detect objects of different shape and surface topology. To evoke tactile sensation at different fingers, a 2x8 electrode grid was placed along the subject's upper arm, and two concurrent electrical stimulation trains targeted the median and ulnar nerve bundles, which evoked individually modulated sensations at different fingers. Fingertip forces of the prosthetic hand were transformed to stimulation current amplitude. Object shape was encoded based on finger-object contact timing. Surface topology represented by ridge height and spacing was encoded through current amplitude and stimulation time interval, respectively. The elicited sensation allowed subjects to determine object shape with success rates >84%. Surface topology recognition resulted in success rates >81%. Our findings suggest that tactile feedback evoked from transcutaneous nerve stimulation allows the recognition of object shape and surface topology. The ability to recognize these properties may help improve object manipulation and promote fine control of a prosthetic hand.}, number={1}, journal={IEEE TRANSACTIONS ON HAPTICS}, author={Vargas, Luis and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2020}, pages={152–158} }
 @article{zheng_hu_2019, title={Elicited Finger and Wrist Extension Through Transcutaneous Radial Nerve Stimulation}, volume={27}, ISSN={["1558-0210"]}, DOI={10.1109/TNSRE.2019.2930669}, abstractNote={Individuals with neurological disorders, such as stroke or spinal cord injury, often have weakness and/or spasticity in their hand and wrist muscles, which can lead to impaired ability to extend their fingers and wrists. Functional electrical stimulation can help to restore these motor functions. However, the conventional stimulation method can lead to fast muscle fatigue and limited movements due to a non-physiological recruitment of motor units and a limited recruitment of deep muscles. In this paper, we investigated the feasibility of eliciting various hand opening and wrist extension movement patterns through a transcutaneous electrical stimulation array, which targeted the proximal segment of the radial nerve bundle proximal to the elbow. The wrist and finger joint kinematics were used to classify the different movement patterns through a cluster analysis, and electromyogram signals from the wrist and finger extensors were recorded to investigate the muscle activation patterns. The results showed that the finger and wrist motions can be elicited both independently and in a coordinated manner, by changing the stimulation intensity and stimulation location. H-reflex activity was also observed, which demonstrated the potential of recruiting motor units in a physiological order. Our approach could be further developed into a rehabilitative/assistive tool for individuals with impaired hand opening and/or wrist extension.}, number={9}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2019}, month={Sep}, pages={1875–1882} }
 @article{vargas_whitehouse_huang_zhu_hu_2019, title={Evoked Haptic Sensation in the Hand With Concurrent Non-Invasive Nerve Stimulation}, volume={66}, ISSN={["1558-2531"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85077396250&partnerID=MN8TOARS}, DOI={10.1109/TBME.2019.2895575}, abstractNote={Objective: Haptic perception is critical for prosthetic users to control their prosthetic hand intuitively. In this study, we seek to evaluate the haptic perception evoked from concurrent stimulation trains through multiple channels using transcutaneous nerve stimulation. Methods: A 2 × 8 electrode grid was used to deliver current to the median and ulnar nerves in the upper arm. Different electrodes were first selected to activate the sensory axons, which can elicit sensations at different locations of the hand. Charge-balanced bipolar stimulation was then delivered to two sets of electrodes concurrently with a phase delay (dual stimulation) to determine whether the evoked sensation can be constructed from sensations of single stimulation delivered separately at different locations (single stimulation) along the electrode grid. The temporal delay between the two stimulation trains was altered to evaluate potential interference. The short-term stability of the haptic sensation within a testing session was also evaluated. Results: The evoked sensation during dual stimulation was largely a direct summation of the sensation from single stimulations. The delay between the two stimulation locations had minimal effect on the evoked sensations, which was also stable over repeated testing within a session. Conclusion: Our results indicated that the haptic sensations at different regions of the hand can be constructed by combining the response from multiple stimulation trains directly. The interference between stimulations were minimal. Significance: The outcomes will allow us to construct specific haptic sensation patterns when the prosthesis interacts with different objects, which may help improve user embodiment of the prosthesis.}, number={10}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Vargas, Luis and Whitehouse, Graham and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2019}, month={Oct}, pages={2761–2767} }
 @article{dai_hu_2019, title={Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings}, volume={29}, ISSN={["1793-6462"]}, DOI={10.1142/S0129065718500259}, abstractNote={ The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed. }, number={1}, journal={INTERNATIONAL JOURNAL OF NEURAL SYSTEMS}, author={Dai, Chenyun and Hu, Xiaogang}, year={2019}, month={Feb} }
 @article{dai_hu_2019, title={Independent component analysis based algorithms for high-density electromyogram decomposition: Experimental evaluation of upper extremity muscles}, volume={108}, ISSN={["1879-0534"]}, DOI={10.1016/j.compbiomed.2019.03.009}, abstractNote={Motor unit firing activities can provide critical information regarding neural control of skeletal muscles. Extracting motor unit activities reliably from surface electromyogram (EMG) is still a challenge in signal processing. We quantified the performance of three different independent component analysis (ICA)-based decomposition algorithms (Infomax, FastICA and RobustICA) on high-density EMG signals, obtained from arm muscles (biceps brachii and extensor digitorum communis) at different contraction levels. The source separation outcomes were evaluated based on the degree of agreement in the discharge timings between different algorithms, and based on the number of common motor units identified concurrently by two algorithms. Two metrics, the separation index (silhouette distance or SIL) and the rate of agreement, were used to evaluate the decomposition accuracy. Our results revealed a high rate of agreement (80%-90%) between different algorithms, which was consistent across different contraction levels. The RobustICA tended to show a higher RoA with the other two algorithms (especially with Infomax), whereas FastICA and Infomax tended to yield a greater number of common MUs. Overall, through an experimental evaluation of the three algorithms, the outcomes provide information regarding the utility of these algorithms and the motor unit filter criteria involving EMG signals of upper extremity muscles.}, journal={COMPUTERS IN BIOLOGY AND MEDICINE}, author={Dai, Chenyun and Hu, Xiaogang}, year={2019}, month={May}, pages={42–48} }
 @article{dai_hu_2019, title={Independent component analysis based algorithms for high-density electromyogram decomposition: Systematic evaluation through simulation}, volume={109}, ISSN={["1879-0534"]}, DOI={10.1016/j.compbiomed.2019.04.033}, abstractNote={Motor unit activities provide important theoretical and clinical insights regarding different aspects of neuromuscular control. Based on high-density electromyogram (HD EMG) recordings, we systematically evaluated the performance of three independent component analysis (ICA)-based EMG decomposition algorithms (Infomax, FastICA and RobustICA). The algorithms were tested on simulated HD EMG signals with a range of muscle contraction levels and with a range of signal quality. Our results showed that all the three algorithms can output accurate (85%-100%) motor unit discharge timings. Specifically, the RobustICA consistently showed high decomposition accuracy among the three algorithms under a variety of signal conditions, especially with a low signal quality and varying contraction levels. But the yield of decomposition of RobustICA tended to be low at high contraction levels. In contrast, FastICA tended to show the lowest accuracy, but can detect the largest number of motor units, especially at high contraction levels. Our results also showed that the computation time was similar for FastICA and RobustICA, which was shorter than Infomax. Additionally, the accuracy of each algorithm correlated moderately with the clustering index-the silhouette distance measure, and correlated strongly with the rate of agreement of the algorithm pairs. Overall, our findings provide guidance on selecting particular decomposition algorithms based on specific applications with different requirement on the accuracy/yield of the decomposition.}, journal={COMPUTERS IN BIOLOGY AND MEDICINE}, author={Dai, Chenyun and Hu, Xiaogang}, year={2019}, month={Jun}, pages={171–181} }
 @article{zheng_hu_2019, title={Interference Removal From Electromyography Based on Independent Component Analysis}, volume={27}, ISSN={["1558-0210"]}, DOI={10.1109/TNSRE.2019.2910387}, abstractNote={High-density surface electromyography (HD-EMG) provides detailed information about muscle activation. However, HD-EMG recordings can be interfered by motion artifacts and power line noise. In this paper, an interference detection and removal method with minimal distortion of the EMG was developed based on the independent component analysis (ICA). After the source separation, the independent components with power line noise were detected based on the spectra and were processed with notch filters. Components with motion artifacts were identified by analyzing the peak frequency of the spectrum, and motion artifacts were filtered with a high-pass filter and an amplitude thresholding method. The EMG signals were then reconstructed based on the processed source signals. The denoising performance was evaluated on both simulated and experimental EMG signals. The results showed that our method was significantly better than the digital filter method and the conventional ICA-based method where components with interferences were set to zero. Namely, our method showed a minimal distortion of the denoised EMG amplitude and frequency and a higher yield of decomposed motor units. Our interference detection and removal algorithm can be used as an effective preprocessing procedure and can benefit macro level EMG analysis and micro level motor unit analysis.}, number={5}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2019}, month={May}, pages={887–894} }
 @article{dai_cao_hu_2019, title={Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities}, volume={47}, ISSN={["1573-9686"]}, DOI={10.1007/s10439-019-02240-1}, abstractNote={Accurate prediction of motor output based on neural signals is critical in human-machine interactions. The objective was to evaluate the performance of predicting individual finger forces through an estimation of the descending neural drive to the spinal motoneuron pool. High-density surface electromyogram (EMG) signals of the extensor digitorum communis muscle were obtained, and were then decomposed into individual motor unit discharge events. The frequency of the composite discharge events at the population level was used to derive the descending neural drive, which was then used to predict the finger forces. The global EMG-based approach was used as a control condition. Compared with the EMG-based approach, the experimental results show that the neural-drive-based approach can better predict the individual finger forces with higher R 2  values across different force levels and across different force trajectories (steady and varying forces). These findings indicate that the neural drive estimation based on motoneuron firing activities can be used as a reliable neural-machine interface signal involving individual fingers. However, real-time implementation of this approach is needed for future clinical translation.}, number={6}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Dai, Chenyun and Cao, Yizhou and Hu, Xiaogang}, year={2019}, month={Jun}, pages={1357–1368} }
 @article{dai_cao_hu_2019, title={Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities (vol 47, pg 1357, 2019)}, volume={47}, ISSN={["1573-9686"]}, DOI={10.1007/s10439-019-02281-6}, abstractNote={Due to an error in production, Figure 4 in the original paper shows the root mean squared error (RMSE) between the force and the neural drive estimation. The R2 heat map as a function of the number of motor units and the accuracy is illustrated below.}, number={7}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Dai, Chenyun and Cao, Yizhou and Hu, Xiaogang}, year={2019}, month={Jul}, pages={1688–1688} }
 @article{son_hu_suresh_rymer_2019, title={Prolonged time course of population excitatory postsynaptic potentials in motoneurons of chronic stroke survivors}, volume={122}, ISSN={["1522-1598"]}, DOI={10.1152/jn.00288.2018}, abstractNote={ Hyperexcitability of spinal motoneurons may contribute to muscular hypertonia after hemispheric stroke. The origins of this hyperexcitability are not clear, but we hypothesized that prolongation of the Ia excitatory postsynaptic potential (EPSP) in spastic motoneurons may be one potential mechanism, by enabling more effective temporal summation of Ia EPSPs, making action potential initiation easier. Thus, the purpose of this study is to quantify the time course of putative EPSPs in spinal motoneurons of chronic stroke survivors. To estimate the EPSP time course, a pair of low-intensity electrical stimuli was delivered sequentially to the median nerve in seven hemispheric stroke survivors and in six intact individuals, to induce an H-reflex response from the flexor carpi radialis muscle. H-reflex response probability was then used to quantify the time course of the underlying EPSPs in the motoneuron pool. A population EPSP estimate was then derived, based on the probability of evoking an H-reflex from the second test stimulus in the absence of a reflex response to the first conditioning stimulus. Our experimental results showed that in six of seven hemispheric stroke survivors, the apparent rate of decay of the population EPSP was markedly slower in spastic compared with contralateral (stroke) and intact motoneuron pools. There was no significant difference in EPSP time course between the contralateral side of stroke survivors and control subject muscles. We propose that one potential mechanism for hyperexcitability of spastic motoneurons in chronic stroke survivors may be associated with this prolongation of the Ia EPSP time course. Our subthreshold double-stimulation approach could provide a noninvasive tool for quantifying the time course of EPSPs in both healthy and pathological conditions. }, number={1}, journal={JOURNAL OF NEUROPHYSIOLOGY}, author={Son, Jongsang and Hu, Xiaogang and Suresh, Nina L. and Rymer, William Z.}, year={2019}, month={Jul}, pages={176–183} }
 @article{zheng_hu_2019, title={Real-time isometric finger extension force estimation based on motor unit discharge information}, volume={16}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/ab2c55}, abstractNote={Objective. The goal of this study was to perform real-time estimation of isometric finger extension force using the discharge information of motor units (MUs). Approach. A real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed the neural-drive method. The conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed the EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-time decomposition. Experimental EMG recordings of 5 min of isometric finger extension with pseudorandom force levels were used to assess the performance of force estimation over time. Main results. The simulation results showed that the accuracy of real-time decomposition was 86%, compared with an offline accuracy of 94%. However, the real-time decomposition accuracy was stable over time. The experimental results showed that the neural-drive method had a significantly smaller root mean square error (RMSE) of the force estimation compared with the EMG-amplitude method, which was consistent across fingers. Additionally, the RMSE of the neural-drive method was stable until 230 s, while the RMSE of the EMG-amplitude method increased progressively over time. Significance. The neural-drive method on real-time finger force estimation was more accurate over time compared with the conventional EMG-amplitude method during prolonged muscle contractions. The outcomes can potentially offer a more accurate and robust neural interface technique for reliable neural-machine interactions based on MU pool discharge information.}, number={6}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2019}, month={Dec} }
 @article{yao_vargas_hu_zhu_2018, title={A Novel Finger Kinematic Tracking Method Based on Skin-Like Wearable Strain Sensors}, volume={18}, ISSN={["1558-1748"]}, DOI={10.1109/jsen.2018.2802421}, abstractNote={Deficits in hand function are common in a majority of stroke survivors. Although hand performance can be routinely assessed during rehabilitation training, a lack of hand usage information during daily activities could prevent clinicians or therapists from making informative therapeutic decisions. In this paper, we demonstrated and validated the application of silver nanowire-based capacitive strain sensors for finger kinematic tracking. The fabricated strain sensors show high sensitivity (gauge factor close to one), low hysteresis, good linearity, large stretchability (150%), and skin-like mechanical property (Young’s modulus of 96 kPa). All these features allow the sensors to be conformally attached onto the skin to track finger joint movement with minimal interference to daily activities. Recordings of the skin deformation from the strain sensors and joint angles from reflective markers are highly correlated (>93%) for different joint oscillation speeds in a stroke survivor and a control subject, indicating the high accuracy of the strain sensors in joint motion tracking. With the wearable silver nanowire-based strain sensors, accurate hand utility information on the impaired hand of stroke survivors can be acquired in a continuous and unobtrusive manner.}, number={7}, journal={IEEE SENSORS JOURNAL}, author={Yao, Shanshan and Vargas, Luis and Hu, Xiaogang and Zhu, Yong}, year={2018}, month={Apr}, pages={3010–3015} }
 @article{shin_chen_hu_2018, title={Delayed fatigue in finger flexion forces through transcutaneous nerve stimulation}, volume={15}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/aadd1b}, abstractNote={Objective. Weakness of the hand is a major impairment which limits independent living. Neuromuscular electrical stimulation (NMES) is a common approach to help restore muscle strength. Traditional NMES directly over the muscle often leads to a rapid onset of muscle fatigue. In this study, we investigated the force sustainability of finger flexor muscles using a transcutaneous nerve stimulation approach. Approach. Finger flexion forces and high-density electromyogram (HD EMG) signals were obtained while electrical stimulation was applied to the ulnar and median nerve bundles through a stimulation grid on the proximal arm segment. Stimulation was also applied to the finger flexor muscle belly targeting the motor point, serving as a control condition. The force produced from the two stimulation approaches were initially matched, and muscle fatigue was subsequently induced with 5 min of continuous stimulation. The rate of decay of the force and EMG amplitude were quantified, and the spatial distribution of the muscle activation during the sustained contraction was also evaluated. Main results. The proximal nerve stimulation approach induced a slower decay in both force and EMG, compared with the stimulation at the motor point. The spatial distribution of the elicited muscle activation showed that the proximal nerve stimulation led to a distributed activation across the intrinsic and extrinsic finger flexor muscles and also activated a wider area within the extrinsic muscle. Significance. Our findings demonstrated that the stimulation of the proximal nerve bundles can elicit sustained force output and delayed decrease in the rate of force decline. This is potentially due to a spatially distributed activation of the muscle fibers, compared with the traditional motor point stimulation. Future development of our nerve stimulation approach may enable prolonged usage during rehabilitation or assistance for better functional outcomes.}, number={6}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Shin, Henry and Chen, Ryan and Hu, Xiaogang}, year={2018}, month={Dec} }
 @article{dai_zheng_hu_2018, title={Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor}, volume={9}, ISSN={["1664-2295"]}, DOI={10.3389/fneur.2018.00187}, abstractNote={Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.}, journal={FRONTIERS IN NEUROLOGY}, author={Dai, Chenyun and Zheng, Yang and Hu, Xiaogang}, year={2018}, month={Mar} }
 @article{shin_watkins_huang_zhu_hu_2018, title={Evoked haptic sensations in the hand via non-invasive proximal nerve stimulation}, volume={15}, ISSN={["1741-2552"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049836114&partnerID=MN8TOARS}, DOI={10.1088/1741-2552/aabd5d}, abstractNote={Objective. Haptic perception of a prosthetic limb or hand is a crucial, but often unmet, need which impacts the utility of the prostheses. In this study, we seek to evaluate the feasibility of a non-invasive transcutaneous nerve stimulation method in generating haptic feedback in a transradial amputee subject as well as intact able-bodied subjects. Approach. An electrode grid was placed on the skin along the medial side of the upper arm beneath the short head of the biceps brachii, in proximity to the median and ulnar nerves. Varying stimulation patterns were delivered to different electrode pairs, in order to emulate different types of sensations (Single Tap, Press-and-Hold, Double Tap) at different regions of the hand. Subjects then reported the magnitude of sensation by pressing on a force transducer to transform the qualitative haptic perception into a quantitative measurement. Main results. Altering current stimulations through electrode pairs on the grid resulted in repeatable alterations in the percept regions of the hand. Most subjects reported spatial coverage of individual fingers or phalanges, which can resemble the whole hand through different pairs of stimulation electrodes. The different stimulation patterns were also differentiable by all subjects. The amputee subject also reported haptic sensations similar to the able-bodied subjects. Significance. Our findings demonstrated the capabilities of our transcutaneous stimulation method. Subjects were able to perceive spatially distinct sensations with graded magnitudes that emulated tapping and holding sensation in their hands. The elicitation of haptic sensations in the phantom hand of an amputee is a significant step in the development of our stimulation method, and provides insight into the future adaptation and implementation of prostheses with non-invasive sensory feedback to the users.}, number={4}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Shin, Henry and Watkins, Zach and Huang, He and Zhu, Yong and Hu, Xiaogang}, year={2018}, month={Aug} }
 @article{zheng_hu_2018, title={Improved muscle activation using proximal nerve stimulation with subthreshold current pulses at kilohertz-frequency}, volume={15}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/aab90f}, abstractNote={Objective. Transcutaneous electrical nerve stimulation can help individuals with neurological disorders to regain their motor function by activating muscles externally. However, conventional stimulation technique often induces near-simultaneous recruitment of muscle fibers, leading to twitch forces time-locked to the stimulation. Approach. To induce less synchronized activation of finger flexor muscles, we delivered clustered narrower pulses to the proximal segment of the median and ulnar nerves at a carrier frequency of either 10 kHz (with an 80 µs pulse width) or 7.14 kHz (with a 120 µs pulse width) (high-frequency mode, HF), and different clustered pulses were delivered at a frequency of 30 or 40 Hz. Conventional stimulation with pulse frequency of 30 or 40 Hz (low-frequency mode, LF) was used for comparison. With matched elicited muscle forces between the HF and LF modes, the force variation, the high-density electromyogram (EMG) signals recorded at the finger flexor muscles and stimulation-induced-pain levels were compared. Main results. The compound action potentials in the 10 kHz HF mode revealed a significant difference (i.e. a lower amplitude and area, and a wider duration) compared with the LF mode, indicating cancellations of asynchronized action potentials. A smaller fluctuation in the elicited forces in the 10 kHz mode further demonstrated the less synchronized activation of different motor units. These effects tended to be weaker in the 7.14 kHz HF condition. However, the levels of pain sensation was not reduced in the HF mode potentially due to the high charge density used in the HF mode. Our findings indicated that different nerve fibers were recruited asynchronously through summations of different numbers of subthreshold depolarizations in the HF mode. Significance. Compared with the LF mode, the HF mode stimulation was capable of activating the nerve fibers in a less synchronized way, which is more similar to the physiological activation pattern.}, number={4}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2018}, month={Aug} }
 @article{zheng_shin_hu_2018, title={Muscle Fatigue Post-stroke Elicited From Kilohertz-Frequency Subthreshold Nerve Stimulation}, volume={9}, ISSN={["1664-2295"]}, DOI={10.3389/fneur.2018.01061}, abstractNote={Purpose: Rapid muscle fatigue limits clinical applications of functional electrical stimulation (FES) for individuals with motor impairments. This study aimed to characterize the sustainability of muscle force elicited with a novel transcutaneous nerve stimulation technique. Method: A hemiplegic chronic stroke survivor was recruited in this case study. Clustered subthreshold pulses of 60-μs with kilohertz (12.5 kHz) carrier frequency (high-frequency mode, HF) were delivered transcutaneously to the proximal segment of the median/ulnar nerve bundles to evaluate the finger flexor muscle fatigue on both sides of the stroke survivor. Conventional nerve stimulation technique with 600-μs pulses at 30 Hz (low-frequency mode, LF) served as the control condition. Fatigue was evoked by intermittently delivering 3-s stimulation trains with 1-s resting. For fair comparison, initial contraction forces (approximately 30% of the maximal voluntary contraction) were matched between the HF and LF modes. Muscle fatigue was evaluated through elicited finger flexion forces (amplitude and fluctuation) and muscle activation patterns quantified by high-density electromyography (EMG). Result: Compared with those from the LF stimuli, the elicited forces declined more slowly, and the force plateau was higher under the HF stimulation for both the affected and contralateral sides, resulting in a more sustainable force output at higher levels. Meanwhile, the force fluctuation under the HF stimulation increased more slowly, and, thus, was smaller after successive stimulation trains compared with the LF stimuli, indicating a less synchronized activation of muscle fibers. The efficiency of the muscle activation, measured as the force-EMG ratio, was also higher in the HF stimulation mode. Conclusion: Our results indicated that the HF nerve stimulation technique can reduce muscle fatigue in stroke survivors by maintaining a higher efficiency of muscle activations compared with the LF stimulation. The technique can help improve the performance of neurorehabilitation methods based on electrical stimulation, and facilitate the utility of FES in clinical populations.}, journal={FRONTIERS IN NEUROLOGY}, author={Zheng, Yang and Shin, Henry and Hu, Xiaogang}, year={2018}, month={Dec} }
 @article{zheng_hu_2018, title={Reduced muscle fatigue using kilohertz-frequency subthreshold stimulation of the proximal nerve}, volume={15}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/aadecc}, abstractNote={Objective. Conventional electrical stimulation techniques targeting the motor points often induce early muscle fatigue onset that can limit clinical applications. In our current study, we evaluated the muscle activation and force generation during fatigue using a novel stimulation technique. Approach. Clustered subthreshold 80 μs current pulses at 10 kHz (high-frequency mode, HF) were delivered transcutaneously to activate the median and ulnar nerve bundles and induce dispersed activations of motor units. Conventional stimulation technique with 800 μs pulses at 30 Hz (low-frequency mode, LF) served as a control condition. Fatigue was evoked by delivering the stimuli continuously for 5 min, with matched initial contraction force (approximately 30% of maximal voluntary contraction) between the HF and LF modes. The elicited finger flexion forces and the muscle activation patterns quantified by high-density electromyogram (EMG) from the finger flexor muscles were compared. Main results. Our results revealed that the elicited force was prolonged under the HF stimulation mode, manifested as a slower decay of the force, a smaller absolute force decline, a higher force plateau, and a larger force-time integral, in comparison with the LF mode. The force-to-EMG ratio under the HF stimulation was also consistently higher than that under the LF mode. In addition, the EMG spatial distribution showed that the muscle activation tended to be more spread-out under the HF mode compared with the LF mode. These results indicated that the HF stimulation induced a higher efficiency of muscle activation, which can potentially reduce muscle fatigue. Significance. Our findings revealed that the subthreshold kilohertz nerve stimulation can induce temporally and spatially dispersed activation of different motor units with more efficient activation patterns. The reduced muscle fatigue can have a prominent advantage in neural rehabilitation involving transcutaneous electrical nerve stimulations.}, number={6}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Zheng, Yang and Hu, Xiaogang}, year={2018}, month={Dec} }
 @article{shin_suresh_rymer_hu_2018, title={Relative contribution of different altered motor unit control to muscle weakness in stroke: a simulation study}, volume={15}, ISSN={["1741-2552"]}, DOI={10.1088/1741-2552/aa925d}, abstractNote={Objective. Chronic muscle weakness impacts the majority of individuals after a stroke. The origins of this hemiparesis is multifaceted, and an altered spinal control of the motor unit (MU) pool can lead to muscle weakness. However, the relative contribution of different MU recruitment and discharge organization is not well understood. In this study, we sought to examine these different effects by utilizing a MU simulation with variations set to mimic the changes of MU control in stroke. Approach. Using a well-established model of the MU pool, this study quantified the changes in force output caused by changes in MU recruitment range and recruitment order, as well as MU firing rate organization at the population level. We additionally expanded the original model to include a fatigue component, which variably decreased the output force with increasing length of contraction. Differences in the force output at both the peak and fatigued time points across different excitation levels were quantified and compared across different sets of MU parameters. Main results. Across the different simulation parameters, we found that the main driving factor of the reduced force output was due to the compressed range of MU recruitment. Recruitment compression caused a decrease in total force across all excitation levels. Additionally, a compression of the range of MU firing rates also demonstrated a decrease in the force output mainly at the higher excitation levels. Lastly, changes to the recruitment order of MUs appeared to minimally impact the force output. Significance. We found that altered control of MUs alone, as simulated in this study, can lead to a substantial reduction in muscle force generation in stroke survivors. These findings may provide valuable insight for both clinicians and researchers in prescribing and developing different types of therapies for the rehabilitation and restoration of lost strength after stroke.}, number={1}, journal={JOURNAL OF NEURAL ENGINEERING}, author={Shin, Henry and Suresh, Nina L. and Rymer, William Zev and Hu, Xiaogang}, year={2018}, month={Feb} }
 @article{dai_suresh_suresh_rymer_hu_2017, title={Altered Motor Unit Discharge Coherence in Paretic Muscles of Stroke Survivors}, volume={8}, ISSN={["1664-2295"]}, DOI={10.3389/fneur.2017.00202}, abstractNote={After a cerebral stroke, a series of changes at the supraspinal and spinal nervous system can alter the control of muscle activation, leading to persistent motor impairment. However, the relative contribution of these different levels of the nervous system to impaired muscle activation is not well understood. The coherence of motor unit (MU) spike trains is considered to partly reflect activities of higher level control, with different frequency band representing different levels of control. Accordingly, the objective of this study was to quantify the different sources of contribution to altered muscle activation. We examined the coherence of MU spike trains decomposed from surface electromyogram (sEMG) of the first dorsal interosseous muscle on both paretic and contralateral sides of 14 hemispheric stroke survivors. sEMG was obtained over a range of force contraction levels at 40, 50, and 60% of maximum voluntary contraction. Our results showed that MU coherence increased significantly in delta (1–4 Hz), alpha (8–12 Hz), and beta (15–30 Hz) bands on the affected side compared with the contralateral side, but was maintained at the same level in the gamma (30–60 Hz) band. In addition, no significant alteration was observed across medium–high force levels (40–60%). These results indicated that the common synaptic input to motor neurons increased on the paretic side, and the increased common input can originate from changes at multiple levels, including spinal and supraspinal levels following a stroke. All these changes can contribute to impaired activation of affected muscles in stroke survivors. Our findings also provide evidence regarding the different origins of impaired muscle activation poststroke.}, journal={FRONTIERS IN NEUROLOGY}, author={Dai, Chenyun and Suresh, Nina L. and Suresh, Aneesha K. and Rymer, William Zev and Hu, Xiaogang}, year={2017}, month={May} }
 @article{shin_watkins_hu_2017, title={Exploration of Hand Grasp Patterns Elicitable Through Non-Invasive Proximal Nerve Stimulation}, volume={7}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-017-16824-1}, abstractNote={Abstract}, journal={SCIENTIFIC REPORTS}, author={Shin, Henry and Watkins, Zach and Hu, Xiaogang}, year={2017}, month={Nov} }
 @article{mcmanus_hu_rymer_suresh_lowery_2017, title={Motor Unit Activity during Fatiguing Isometric Muscle Contraction in Hemispheric Stroke Survivors}, volume={11}, ISSN={["1662-5161"]}, DOI={10.3389/fnhum.2017.00569}, abstractNote={Enhanced muscle weakness is commonly experienced following stroke and may be accompanied by increased susceptibility to fatigue. To examine the contributions of central and peripheral factors to isometric muscle fatigue in stroke survivors, this study investigates changes in motor unit (MU) mean firing rate, and action potential duration during, and directly following, a sustained submaximal fatiguing contraction at 30% maximum voluntary contraction (MVC). A series of short contractions of the first dorsal interosseous muscle were performed pre- and post-fatigue at 20% MVC, and again following a 10-min recovery period, by 12 chronic stroke survivors. Individual MU firing times were extracted using surface EMG decomposition and used to obtain the spike-triggered average MU action potential waveforms. During the sustained fatiguing contraction, the mean rate of change in firing rate across all detected MUs was greater on the affected side (-0.02 ± 0.03 Hz/s) than on the less-affected side (-0.004 ± 0.003 Hz/s, p = 0.045). The change in firing rate immediately post-fatigue was also greater on the affected side than less-affected side (-13.5 ± 20 and 0.1 ± 19%, p = 0.04). Mean MU firing rates increased following the recovery period on the less-affected side when compared to the affected side (19.3 ± 17 and 0.5 ± 20%, respectively, p = 0.03). MU action potential duration increased post-fatigue on both sides (10.3 ± 1.2 to 11.2 ± 1.3 ms on the affected side and 9.9 ± 1.7 to 11.2 ± 1.9 ms on the less-affected side, p = 0.001 and p = 0.02, respectively), and changes in action potential duration tended to be smaller in subjects with greater impairment (p = 0.04). This study presents evidence of both central and peripheral fatigue at the MU level during isometric fatiguing contraction for the first time in stroke survivors. Together, these preliminary observations indicate that the response to an isometric fatiguing contraction differs between the affected and less-affected side post-stroke, and may suggest that central mechanisms observed here as changes in firing rate are the dominant processes leading to task failure on the affected side.}, journal={FRONTIERS IN HUMAN NEUROSCIENCE}, author={McManus, Lara and Hu, Xiaogang and Rymer, William Z. and Suresh, Nina L. and Lowery, Madeleine M.}, year={2017}, month={Nov} }
 @article{dai_shin_davis_hu_2017, title={Origins of Common Neural Inputs to Different Compartments of the Extensor Digitorum Communis Muscle}, volume={7}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-017-14555-x}, abstractNote={Abstract}, journal={SCIENTIFIC REPORTS}, author={Dai, Chenyun and Shin, Henry and Davis, Bradley and Hu, Xiaogang}, year={2017}, month={Oct} }
 @article{mcmanus_hu_rymer_suresh_lowery_2016, title={Muscle fatigue increases beta-band coherence between the firing times of simultaneously active motor units in the first dorsal interosseous muscle}, volume={115}, ISSN={["1522-1598"]}, DOI={10.1152/jn.00097.2016}, abstractNote={ Synchronization between the firing times of simultaneously active motor units (MUs) is generally assumed to increase during fatiguing contractions. To date, however, estimates of MU synchronization have relied on indirect measures, derived from surface electromyographic (EMG) interference signals. This study used intramuscular coherence to investigate the correlation between MU discharges in the first dorsal interosseous muscle during and immediately following a submaximal fatiguing contraction, and after rest. Coherence between composite MU spike trains, derived from decomposed surface EMG, were examined in the delta (1–4 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–60 Hz) frequency band ranges. A significant increase in MU coherence was observed in the delta, alpha, and beta frequency bands postfatigue. In addition, wavelet coherence revealed a tendency for delta-, alpha-, and beta-band coherence to increase during the fatiguing contraction, with subjects exhibiting low initial coherence values displaying the greatest relative increase. This was accompanied by an increase in MU short-term synchronization and a decline in mean firing rate of the majority of MUs detected during the sustained contraction. A model of the motoneuron pool and surface EMG was used to investigate factors influencing the coherence estimate. Simulation results indicated that changes in motoneuron inhibition and firing rates alone could not directly account for increased beta-band coherence postfatigue. The observed increase is, therefore, more likely to arise from an increase in the strength of correlated inputs to MUs as the muscle fatigues. }, number={6}, journal={JOURNAL OF NEUROPHYSIOLOGY}, author={McManus, Lara and Hu, Xiaogang and Rymer, William Z. and Suresh, Nina L. and Lowery, Madeleine M.}, year={2016}, month={Jun}, pages={2830–2839} }