The aim of this study was to compare the decomposition results obtained from high-density surface electromyography (EMG) and concurrently recorded intramuscular EMG. Surface EMG signals were recorded with electrode grids from the tibialis anterior, biceps brachii, and abductor digiti minimi muscles of twelve healthy men during isometric contractions ranging between 5% and 20% of the maximal force. Bipolar intramuscular EMG signals were recorded with pairs of wire electrodes. Surface and intramuscular EMG were independently decomposed into motor unit spike trains. When averaged over all the contractions of the same contraction force, the percentage of discharge times of motor units identified by both decompositions varied in the ranges 84%–87% (tibialis anterior), 84%–86% (biceps brachii), and 87%–92% (abductor digiti minimi) across the force levels analyzed. This index of agreement between the two decompositions was linearly correlated with a self-consistency measure of motor unit discharge pattern that was based on coefficient of variation for the interspike interval (${ R} ^{2} = 0.68$ for tibialis anterior, ${ R} ^{2} = 0.56$ for biceps brachii, and ${ R} ^{2} = 0.38$ for abductor digiti minimi). These results constitute an important contribution to the validation of the noninvasive approach for the investigation of motor unit behavior in isometric low-force tasks.

This study proposes a novel auditory brain–computer interface paradigm, which allows the subject to mentally select a target among a random sequence of spoken digits. The subject's voluntary recognition of the property of the target digits enhances the discriminability between brain responses to target and nontarget digits. EEG data from 14 subjects has shown that the amplitude of N2 and the late positive component (LPC) elicited by target digits was significantly higher than that of nontarget ones. Three classification methods, i.e., N2/LPC area comparison, Fisher discriminant analysis and support vector machine (SVM), were adopted to assess the target detection accuracy using EEG data from a single electrode. For SVM classification, a mean accuracy of 85% was achieved with five trials averaged. This new paradigm could be useful for locked-in patients with vision impairment.

Many new assistive devices are available for individuals paralyzed below the neck due to spinal cord injury. Severely paralyzed individuals must be able to command their complex assistive devices using remaining activity from the neck up. Electromyographic (EMG) sensors enable people to use contractions of head and neck muscles to generate multiple proportional command signals. Electroencephalographic (EEG) signals can also be used to generate commands for assistive device control by conveying information about imagined or attempted movements. Fully-implanted wireless biopotential detection systems are now being developed to reliably detect EMGs, EEGs, or a mixture of the two from recording electrodes implanted just under the skin or scalp thus eliminating the need for externally worn hardware on the head or face. This present study shows how novel patterns of jaw muscle contractions, detected via biopotential sensors on the scalp surface or implanted just under the scalp, can be used to generate reliable discrete EMG commands, which can be differentiated from patterns generated during normal activities, such as chewing. These jaw contractions can be detected with sensors already in place to detect other muscle- or brain-based command signals thus adding to the functionality of current device control systems.

Somatosensory evoked potential (SEP) usually contains a set of detailed temporal components measured and identified in time domain, providing meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to reveal complex and fine time-frequency features of SEP in time-frequency domain using advanced time-frequency analysis (TFA) and pattern classification methods. A high-resolution TFA algorithm, matching pursuit (MP), was proposed to decompose a SEP signal into a string of elementary waves and to provide a time-frequency feature description of the waves. After a dimension reduction by principle component analysis (PCA), a density-guided K-means clustering was followed to identify typical waves existed in SEP. Experimental results on posterior tibial nerve SEP signals of 50 normal adults showed that a series of typical waves were discovered in SEP using the proposed MP decomposition and clustering methods. The statistical properties of these SEP waves were examined and their representative waveforms were synthesized. The identified SEP waves provided a comprehensive and detailed description of time-frequency features of SEP.

Transcutaneous electrical stimulation (TES) is a technique to artificially activate motor nerves and muscles. It can be used for rehabilitation or the restoration of lost motor functions, e.g., in subjects with brain or spinal cord lesions. Apart from selectively activating motor nerves and muscles, TES activates sensory fibers and pain receptors, producing discomfort and pain. Clinicians try to minimize discomfort by optimizing stimulation parameters, electrode location, and electrode size. There are some studies that found optimal electrode sizes for certain stimulation sites (e.g., gastrocnemius), however the underlying effects why certain electrode sizes are preferred by patients is not well understood. We used a TES model consisting of a finite element (FE) model and a nerve model to assess the influence of different electrode sizes on the selectivity and the perceived comfort for various anatomies. Motor thresholds calculated using the TES model were compared with motor thresholds that were obtained from measurements performed on the forearm of ten human volunteers. Results of the TES model indicate that small electrodes (0.8$,times,$0.8 ${hbox {cm}}^{2}$) are more comfortable for thin fat layers (0.25 cm) and superficial nerves (0.1 cm) and larger electrodes (4.1$,times,$ 4.1 ${hbox {cm}}^{2}$ ) are more comfortable for thicker fat layers (2 cm) and deeper nerves (1.1 cm) at a constant recruitment.

While neuroscientists identify increasingly complex neural circuits that control animal and human gait, biomechanists find that locomotion requires little control if principles of legged mechanics are heeded that shape and exploit the dynamics of legged systems. Here, we show that muscle reflexes could be vital to link these two observations. We develop a model of human locomotion that is controlled by muscle reflexes which encode principles of legged mechanics. Equipped with this reflex control, we find this model to stabilize into a walking gait from its dynamic interplay with the ground, reproduce human walking dynamics and leg kinematics, tolerate ground disturbances, and adapt to slopes without parameter interventions. In addition, we find this model to predict some individual muscle activation patterns known from walking experiments. The results suggest not only that the interplay between mechanics and motor control is essential to human locomotion, but also that human motor output could for some muscles be dominated by neural circuits that encode principles of legged mechanics.

A phase-locked loop (PLL) model of the response of the postural control system to periodic platform motion is proposed. The PLL model is based on the hypothesis that quiet standing (QS) postural sway can be characterized as a weak sinusoidal oscillation corrupted with noise. Because the signal to noise ratio is quite low, the characteristics of the QS oscillator are not measured directly from the QS sway, instead they are inferred from the response of the oscillator to periodic motion of the platform. When a sinusoidal stimulus is applied, the QS oscillator changes speed as needed until its frequency matches that of the platform, thus achieving phase lock in a manner consistent with a PLL control mechanism. The PLL model is highly effective in representing the frequency, amplitude, and phase shift of the sinusoidal component of the phase-locked response over a range of platform frequencies and amplitudes. Qualitative analysis of the PLL control mechanism indicates that there is a finite range of frequencies over which phase lock is possible, and that the size of this capture range decreases with decreasing platform amplitude. The PLL model was tested experimentally using nine healthy subjects and the results reveal good agreement with a mean phase shift error of 13.7° and a mean amplitude error of 0.8 mm.

As stroke incidence increases, therapists' time is under pressure. Technology-supported rehabilitation may offer new opportunities. The objective of this study was to evaluate patient motivation for and the feasibility and effects of a new technology-supported task-oriented arm training regime (T-TOAT). Nine chronic stroke patients performed T-TOAT (2$,times,$ 30 min/day, four days/week) during eight weeks. A system including movement tracking sensors, exercise board, and software-based toolkit was used for skill training. Measures were recorded at baseline, after four and eight weeks of training, and six months posttraining. T-TOAT improved arm–hand performance significantly on Fugl–Meyer, Action Research Arm Test, and Motor Activity Log. Training effects lasted at least six months posttraining. Health-related-quality-of-life had improved significantly after eight weeks of T-TOAT with regard to perceived physical health, but not to perceived mental health (SF-36). None of the EuroQol-5D components showed significant differences before and after training. Participants were intrinsically motivated and felt competent to use the system. Furthermore, system usability was rated very good. However, exercise challenge as perceived by participants decreased significantly over eight weeks of training. The results of this study indicate that T-TOAT is feasible. Despite the small number of stroke patients tested, significant and clinically relevant improvements in skilled arm–hand performance were found.

In this paper we present a universal haptic drive (UHD), a device that enables rehabilitation of either arm (“ARM” mode) or wrist (“WRIST” mode) movement in two degrees-of-freedom. The mode of training depends on the selected mechanical configuration, which depends on locking/unlocking of a passive universal joint. Actuation of the device is accomplished by utilizing a series elastic actuation principle, which enables use of off-the-shelf mechanical and actuation components. A proportional force control scheme, needed for implementation of impedance control based movement training, was implemented. The device performance in terms of achievable lower and upper bound of viable impedance range was evaluated through adequately chosen sinusoidal movement in eight directions of a planar movement for the “ARM” mode and in eight directions of a combined wrist flexion/extension and forearm pronation/supination movement for the “WRIST” mode. Additionally, suitability of the universal haptic drive for movement training was tested in a series of training sessions conducted with a chronic stroke subject. The results have shown that reliable and repeatable performance can be achieved in both modes of operation for all tested directions.

Timed Up and Go (TUG) test is a widely used clinical paradigm to evaluate balance and mobility. Although TUG includes several complex subcomponents, namely: sit-to-stand, gait, 180$^{circ}$ turn, and turn-to-sit; the only outcome is the total time to perform the task. We have proposed an instrumented TUG, called iTUG, using portable inertial sensors to improve TUG in several ways: automatic detection and separation of subcomponents, detailed analysis of each one of them and a higher sensitivity than TUG. Twelve subjects in early stages of Parkinson's disease (PD) and 12 age matched control subjects were enrolled. Stopwatch measurements did not show a significant difference between the two groups. The iTUG, however, showed a significant difference in cadence between early PD and control subjects (111.1 $pm$ 6.2 versus 120.4 $pm$ 7.6 step/min, $p<0.006$ ) as well as in angular velocity of arm-swing (123 $pm$ 32.0 versus $174.0pm 50.4^{circ}/{rm s}$, $p<0.005$), turning duration (2.18 $pm$ 0.43 versus 1.79 $pm$ 0.27 s, $p<0.023$ ), and time to perform turn-to-sits (2.96 $pm$ 0.68 versus 2.40 $pm$ 0.33 s, $p<0.023$). By repeating the tests for a second time, the test–retest reliability of iTUG was also evaluated. Among the subcomponents of iTUG, gait, turning, and turn-to-sit were the most reliable and sit-to-stand was the least reliable.

Tactile mice, computer mice modified to have tactile pin displays on their upper surface, have been developed to enable access to 2-D graphical information for individuals who are blind or visually impaired; however, they have yet to really be adopted by the community. We suggest that this is due to the significant lack of accuracy in the haptic position information, which is critical for individuals to haptically piece together a 2-D graphic. We have identified two main design issues that affect this accuracy. Making simple modifications to correct these problems, we show a significant improvement in performance.

Stimulation efficiency is an important consideration in the stimulation parameters of implantable neural stimulators. The objective of this study was to analyze the effects of waveform shape and duration on the charge, power, and energy efficiency of neural stimulation. Using a population model of mammalian axons and in vivo experiments on cat sciatic nerve, we analyzed the stimulation efficiency of four waveform shapes: square, rising exponential, decaying exponential, and rising ramp. No waveform was simultaneously energy-, charge-, and power-optimal, and differences in efficiency among waveform shapes varied with pulse width $(PW)$. For short $PW$s ( $leq$0.1 ms), square waveforms were no less energy-efficient than exponential waveforms, and the most charge-efficient shape was the ramp. For long $PW$ s ($geq$ 0.5 ms), the square was the least energy-efficient and charge-efficient shape, but across most $PW$ s, the square was the most power-efficient shape. Rising exponentials provided no practical gains in efficiency over the other shapes, and our results refute previous claims that the rising exponential is the energy-optimal shape. An improved understanding of how stimulation parameters affect stimulation efficiency will help improve the design and programming of implantable stimulators to minimize tissue damage and extend battery life.

Intermuscular coherence in the beta band was explored as a possible indicator of vocal hyperfunction, a common condition associated with many voice disorders. Surface electromyography (sEMG) was measured from two electrodes on the anterior neck surface of 18 individuals with vocal nodules and 18 individuals with healthy normal voice. Coherence was calculated from sEMG activity gathered while participants produced both read and spontaneous speech. There was no significant effect of speech type on average coherence. Individuals with vocal nodules showed significantly lower mean coherence in the beta band (15–35 Hz) when compared to controls. Results suggest that bilateral EMG–EMG beta coherence in neck strap muscle during speech production shows promise as an indicator of vocal hyperfunction.