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

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

Motor unit spike trains (MUSTs) decomposed from surface electromyography (sEMG) have been an emerging solution for neural interfacing, especially for the control of upper limb prosthetics. Accurate and efficient decomposition techniques are essential and desirable. However, most decomposition methods are designed for motor units (MUs) with global maximum of single or large muscle, while in general forearm muscles are usually small and slender with low global energy. Thus, we propose a novel approach using local spatial information towards more accurate and efficient sEMG decomposition of forearm muscles. A fast spatial spike detection method is proposed to replace the time-consuming iteration process of blind source separation (BSS) methods. Here, spatial distribution characteristics of motor unit action potential are leveraged to pre-classify the candidate MUs, and further to create initial MU templates, aiming to avoid repeating convergence to high-energy MUs. The results of both simulated and experimental sEMG signals show that low-energy MUs from small muscles are more easily found compared with conventional BSS algorithm. Specifically, the proposed method can identify more 40% reliable MUs while only 30% consuming time are needed. The outcomes provide a novel solution for more efficient sEMG decomposition, potentially paving the way of MUST-based non-invasive neural interface.

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 ± 0.03 and 10.1% ± 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

Surface electromyography (EMG) signals have shown promising applications in human-machine interfacing (HMI) systems such as orthotics, prosthetics, and exoskeletons. Nevertheless, existing myoelectric control methods, generally based on time-domain or frequency-domain features, could not directly interpret neural commands. EMG decomposition techniques have become a prevailing solution to decode the motor neuron discharges from the spinal cord, whereas only single degree-of-freedom (DoF) movements are primarily involved in the current neural-based interfaces, resulting in limited intuitiveness and functionality. Here, we propose a non-invasive framework to analyze motor unit activities and estimate wrist torques during simultaneous contractions of multiple DoFs. Motor unit discharges were decoded from surface EMG signals and pooled into groups during sequential wrist movements. Then three neural features were extracted and linearly projected to the torques of multi-DoF tasks. On average, there were 44 ±13 motor units identified for each motion with a PNR value of 25.8 ±2.9 dB. The neural features outperformed the classic EMG feature on the estimation accuracy with higher correlation coefficients and smoothness. These results demonstrate the feasibility and superiority of the proposed framework in kinetics estimation of simultaneous movements, extending the potential applications of surface EMG decomposition in human-machine interfaces.

Neural interface using decomposed motor units (MUs) from surface electromyography (sEMG) has allowed non-invasive access to the neural control signals, and provided a novel approach for intuitive human-machine interaction. However, most of the existing methods based on decomposed MUs merely adopted the discharge rate (DR) as the feature representations, which may lack local information around the discharge instant and ignore the subtle interactions of different MUs. In this study, we proposed an MU-specific image-based scheme for wrist torque estimation. Specifically, the high-density sEMG signals were decoded into motor unit spike trains (MUSTs), and then MU-specific images were reconstructed with MUSTs and corresponding motor unit action potential (MUAP). A convolutional neural network was used to learn representative features from MU-specific images automatically, and further to estimate wrist torques. The results demonstrated that the proposed method outperformed three conventional and a deep-learning regression approaches using DR features, with the estimation accuracy R2of 0.82 ± 0.09, 0.89 ± 0.06, and nRMSE of 12.6 ± 2.5%, 11.0 ± 3.1% for pronation/supination and flexion/extension, respectively. Further, the analysis of the extracted features from MU-specific images showed a higher correlation than DR for recorded torques, indicating the effectiveness of the proposed method. The outcomes of this study provide a novel and promising perspective for the intuitive control of neural interfacing.

Neural interface using motor units (MUs) decomposed from surface electromyography (sEMG) has provided a novel approach for the intuitive human-robot interaction. However, existing feature extraction methods from decomposed MUs are simplex, ignoring the inherent spatial information and the subtle interactions between different MUs. In this study, we proposed a MU-specific images based scheme for extracting features from decomposed MUs and further estimating wrist torques continuously. Specifically, MU-specific images were reconstructed from decomposed MUs using sEMG and fed into a convolutional neural network for feature extraction and estimating wrist torques. The results demonstrated that the proposed scheme significantly outperformed three conventional regression methods using decomposed spike count features, with R2equal to 0.86 ± 0.05 in pronation/supination and 0.90 ± 0.05 in flexion/extension. This study provides a novel scheme for estimation of continuous movement using decomposed MUs and potentially paves the way of neural interface.