Voluntary muscle force control is accomplished both by recruitment of motor units (MUs) and by firing rate modulation of active MUs. Typically, MU recruitment and firing rate organization is assessed using piecemeal intramuscular recordings drawn from different experiments, or even from different subjects. As a consequence, it is often difficult to assemble a systematic description of the relations between the different MU properties relevant to the control of muscle force. To address this gap, the objective of our current study was to characterize recruitment and firing rate organization of multiple MUs of differing action potential size, recorded simultaneously from the first dorsal interosseous muscle of intact human subjects, using a recently developed surface electromyogram (EMG) sensor array recording and decomposition system (Delsys). We sought to assess the relation between putative MU size and the recruitment and firing properties for these MUs, recorded at different muscle contraction levels. Spike-triggered averaging (STA) of the surface EMG was performed to estimate the action potential sizes using the firing times of discriminated MUs as the event triggers. The results show that the size principle, which relates MU size to recruitment rank order, was clearly evident during individual force contractions. In addition, the mean firing rate across MUs decreased with increasing size of the MU action potential and was also inversely proportional to the recruitment threshold force. We propose that surface EMG recordings together with advanced decomposition systems, combined with STA methods, may provide an efficient way to systematically examine MU pool organizational properties.

In motor tasks with redundancy neuromotor noise can lead to variations in execution while achieving relative invariance in the result. The present study examined whether humans find solutions that are tolerant to intrinsic noise. Using a throwing task in a virtual set-up where an infinite set of angle and velocity combinations at ball release yield throwing accuracy, our computational approach permitted quantitative predictions about solution strategies that are tolerant to noise. Based on a mathematical model of the task expected results were computed and provided predictions about error-tolerant strategies (Hypothesis 1). As strategies can take on a large range of velocities, a second hypothesis was that subjects select strategies that minimize velocity at release to avoid costs associated with signal- or velocity-dependent noise or higher energy demands (Hypothesis 2). Two experiments with different target constellations tested these two hypotheses. Results of Experiment 1 showed that subjects chose solutions with high error-tolerance, although these solutions also had relatively low velocity. These two benefits seemed to outweigh that for many subjects these solutions were close to a high-penalty area, i.e. they were risky. Experiment 2 dissociated the two hypotheses. Results showed that individuals were consistent with Hypothesis 1 although their solutions were distributed over a range of velocities. Additional analyses revealed that a velocity-dependent increase in variability was absent, probably due to the presence of a solution manifold that channeled variability in a task-specific manner. Hence, the general acceptance of signal-dependent noise may need some qualification. These findings have significance for the fundamental understanding of how the central nervous system deals with its inherent neuromotor noise. It is widely recognized that variability or noise is present at all levels of the sensorimotor system. How the central nervous system generates functional behavior with a sufficient degree of accuracy in the face of this noise remains an open question. This is specifically relevant when the motor task is redundant, i.e., where many different executions can achieve the same task goal. Using an experimentally controlled throwing movement as model task we examined how humans acquire movement strategies that are tolerant to intrinsic noise. Based on a new computational approach that parses variability based on an analysis of task redundancy, we tested two hypotheses: 1) Subjects are sensitive to noise and seek solutions that are tolerant to this noise. 2) Subjects avoid solutions with high velocities and the costs associated with high velocities. Analysis of the distributional properties of variability in two experiments revealed that humans select those strategies that maximize error-tolerance. These findings have significance for fundamental understanding of the central nervous system and for learning in the context of rehabilitation.

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 sEMG 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 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 classic 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.

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.

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.

Using recordings from a five-pin surface sensor array, a template-based surface electromyogram (sEMG) decomposition system has been developed to identify single motor unit discharge properties. However, the reliability of such template based decomposition results has not been thoroughly examined except by the developers. The focus of this study was to assess the validity of the motor unit decomposition technique, using EMG recordings from the first dorsal interosseous muscle of able-bodied human subjects.