@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{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{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} }