Intracortical microstimulation (ICMS) has promise as a means for delivering somatosensory feedback in neuroprosthetic systems. Various tactile sensations could be encoded by temporal, spatial, or spatiotemporal patterns of ICMS. However, the applicability of temporal patterns of ICMS to artificial tactile sensation during active exploration is unknown, as is the minimum discriminable difference between temporally modulated ICMS patterns. We trained rhesus monkeys in an active exploration task in which they discriminated periodic pulse-trains of ICMS (200 Hz bursts at a 10 Hz secondary frequency) from pulse trains with the same average pulse rate, but distorted periodicity (200 Hz bursts at a variable instantaneous secondary frequency). The statistics of the aperiodic pulse trains were drawn from a gamma distribution with mean inter-burst intervals equal to those of the periodic pulse trains. The monkeys distinguished periodic pulse trains from aperiodic pulse trains with coefficients of variation 0.25 or greater. Reconstruction of movement kinematics, extracted from the activity of neuronal populations recorded in the sensorimotor cortex concurrent with the delivery of ICMS feedback, improved when the recording intervals affected by ICMS artifacts were removed from analysis. These results add to the growing evidence that temporally patterned ICMS can be used to simulate a tactile sense for neuroprosthetic devices.

The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r2) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.

We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5-μm standard CMOS process and consumes 4.5 mW from ±1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of ~ 2.26 Mb/s.

This paper presents the framework for developing a robotic system to improve accuracy and reliability of clinical assessment. Clinical assessment of spasticity tends to have poor reliability because of the nature of the in-person assessment. To improve accuracy and reliability of spasticity assessment, a haptic device, named the HESS (Haptic Elbow Spasticity Simulator) has been designed and constructed to recreate the clinical “feel” of elbow spasticity based on quantitative measurements. A mathematical model representing the spastic elbow joint was proposed based on clinical assessment using the Modified Ashworth Scale (MAS) and quantitative data (position, velocity, and torque) collected on subjects with elbow spasticity. Four haptic models (HMs) were created to represent the haptic feel of MAS 1, 1+, 2, and 3. The four HMs were assessed by experienced clinicians; three clinicians performed both in-person and haptic assessments, and had 100% agreement in MAS scores; and eight clinicians who were experienced with MAS assessed the four HMs without receiving any training prior to the test. Inter-rater reliability among the eight clinicians had substantial agreement (κ = 0.626). The eight clinicians also rated the level of realism (7.63 ± 0.92 out of 10) as compared to their experience with real patients.

Previous investigations of feedback control of standing after spinal cord injury (SCI) using functional neuromuscular stimulation (FNS) have primarily targeted individual joints. This study assesses the potential efficacy of comprehensive (trunk, hips, knees, and ankles) joint-feedback control against postural disturbances using a bipedal, three-dimensional computer model of SCI stance. Proportional-derivative feedback drove an artificial neural network trained to produce muscle excitation patterns consistent with maximal joint stiffness values achievable about neutral stance given typical SCI muscle properties. Feedback gains were optimized to minimize upper extremity (UE) loading required to stabilize against disturbances. Compared to the baseline case of maximum constant muscle excitations used clinically, the controller reduced UE loading by 55% in resisting external force perturbations and by 84% during simulated one-arm functional tasks. Performance was most sensitive to inaccurate measurements of ankle plantar/dorsiflexion position and hip ab/adduction velocity feedback. In conclusion, comprehensive joint-feedback demonstrates potential to markedly improve FNS standing function. However, alternative control structures capable of effective performance with fewer sensor-based feedback parameters may better facilitate clinical usage.

The peripheral nervous system carries sensory and motor information that could be useful as command signals for function restoration in areas such as neural prosthetics and Functional Electrical Stimulation (FES). Nerve cuff electrodes provide a robust and safe technique for recording nerve signals. However, a method to separate and recover signals from individual fascicles is necessary. Prior knowledge of the electrode geometry was used to develop an algorithm which assumes neither signal independence nor detailed knowledge of the nerve’s geometry/conductivity, and is applicable to any wide-band near-field situation. When used to recover fascicular activities from simulated nerve cuff recordings in a realistic human femoral nerve model, this beamforming algorithm separates signals as close as 1.5mm with cross-correlation coefficient, R>0.9 (10% noise). Ten simultaneous signals could be recovered from individual fascicles with only a 20% decrease in R compared to a single signal. At high noise levels (40%), sources were localized to 180±170 μm in the 12x3mm cuff. Localizing sources and using the resulting positions in the recovery algorithm yielded R=0.66±0.10 in 10% noise for 5 simultaneous muscle-activation signals from synergistic fascicles. These recovered signals should allow natural, robust, closed-loop control of multiple degree-of-freedom prosthetic devices and FES systems.

Incorporating sensory feedback with prosthetic devices is now possible, but the optimal methods of providing such feedback are still unknown. The relative utility of amplitude and pulse train frequency modulated stimulation paradigms for providing vibrotactile feedback for object manipulation was assessed in 10 participants. The two approaches were studied during virtual object manipulation using a robotic interface as a function of presentation order and a simultaneous cognitive load. Despite the potential pragmatic benefits associated with pulse train frequency modulated vibrotactile stimulation, comparison of the approach with amplitude modulation indicates that amplitude modulation vibrotactile stimulation provides superior feedback for object manipulation.

Intermuscular coherence can identify oscillatory coupling between two electromyographic (EMG) signals, measuring common presynaptic drive to motor neurons. Beta band oscillations (15–30 Hz) are hypothesized to originate largely from primary motor cortex, and are reduced during dynamic relative to static motor tasks. It has yet to be established whether motor imagery modulates beta intermuscular coherence. Using visual feedback, 10 unimpaired participants completed eighteen trials of pinching their right thumb and index finger at a constant force. During the 60-second trials, participants simultaneously engaged in one of three types of kinesthetic imagery: the right thumb and index finger executing a constant force pinch (static), the fingers of the right hand sequentially flexing and extending (dynamic), and the right foot pushing down with constant force (foot). Motor imagery of a dynamic motor task resulted in significantly lower intermuscular beta coherence than imagery of a static motor pinch task, without any difference in task performance or root-mean-square EMG. Thus, motor imagery affects intermuscular coherence in the beta band, even while measures of task performance remain constant. This finding provides insight for incorporation of beta band intermuscular coherence in future motor rehabilitation schemes and brain computer interface design.

Most hand prostheses do not provide intentional haptic feedback about movement performance; thus users must rely almost completely on visual feedback. This paper focuses on understanding the effects of learning and different stimulation sites when vibrotactile stimulation is used as the intentional haptic feedback. Eighteen unimpaired individuals participated in this study with a robotic interface to manipulate a virtual object with visual and vibrotactile feedback at four body sites (finger, arm, neck, and foot) presented in a random order. All participants showed improvements in object manipulation performance with the addition of vibrotactile feedback. Specifically, performance showed a strong learning effect across time, with learning transferring across different sites of vibrotactile stimulation. The effects of learning over the experiment overshadowed the effects of different stimulation sites. The addition of a cognitive task slowed participants and increased the subjective difficulty. User preference ratings showed no difference in their preference between vibrotactile stimulation sites. These findings indicate that the stimulation site may not be as critical as ensuring adequate training with vibrotactile feedback during object manipulation. Future research to identify improvements in vibrotactile-based feedback parameters with amputees is warranted.

The delivery of high-frequency alternating currents has been shown to produce a focal and reversible conduction block in whole nerve and is a potential therapeutic option for various diseases and disorders involving pathological or undesired neurological activity. However, delivery of high-frequency alternating current to a nerve produces a finite burst of neuronal firing, called the onset response, before the nerve is blocked. Reduction or elimination of the onset response is very important to moving this type of nerve block into clinical applications since the onset response is likely to result in undesired muscle contraction and pain. This paper describes a study of the effect of nerve cuff electrode geometry (specifically, bipolar contact separation distance), and waveform amplitude on the magnitude and duration of the onset response. Electrode geometry and waveform amplitude were both found to affect these measures. The magnitude and duration of the onset response showed a monotonic relationship with bipolar separation distance and amplitude. The duration of the onset response varied by as much as 820% on average for combinations of different electrode geometries and waveform amplitudes. Bipolar electrodes with a contact separation distance of 0.5 mm resulted in the briefest onset response on average. Furthermore, the data presented in this study provide some insight into a biophysical explanation for the onset response. These data suggest that the onset response consists of two different phases: one phase which is responsive to experimental variables such as electrode geometry and waveform amplitude, and one which is not and appears to be inherent to the transition to the blocked state. This study has implications for nerve block electrode and stimulation parameter selection for clinical therapy systems and basic neurophysiology studies.

Many medical conditions are characterized by undesired or pathological peripheral neurological activity. The local delivery of high-frequency alternating currents (HFAC) has been shown to be a fast acting and quickly reversible method of blocking neural conduction and may provide a treatment alternative for eliminating pathological neural activity in these conditions. This work represents the first formal study of electrode design for high-frequency nerve block, and demonstrates that the interpolar separation distance for a bipolar electrode influences the current amplitudes required to achieve conduction block in both computer simulations and mammalian whole nerve experiments. The minimal current required to achieve block is also dependent on the diameter of the fibers being blocked and the electrode–fiber distance. Single fiber simulations suggest that minimizing the block threshold can be achieved by maximizing both the bipolar activating function (by adjusting the bipolar electrode contact separation distance) and a synergistic addition of membrane sodium currents generated by each of the two bipolar electrode contacts. For a rat sciatic nerve, 1.0–2.0 mm represented the optimal interpolar distance for minimizing current delivery.

In normal individuals, the vestibular labyrinths sense head movement and mediate reflexes that maintain stable gaze and posture. Bilateral loss of vestibular sensation causes chronic disequilibrium, oscillopsia, and postural instability. We describe a new multichannel vestibular prosthesis (MVP) intended to restore modulation of vestibular nerve activity with head rotation. The device comprises motion sensors to measure rotation and gravitoinertial acceleration, a microcontroller to calculate pulse timing, and stimulator units that deliver constant-current pulses to microelectrodes implanted in the labyrinth. This new MVP incorporates many improvements over previous prototypes, including a 50% decrease in implant size, a 50% decrease in power consumption, a new microelectrode array design meant to simplify implantation and reliably achieve selective nerve-electrode coupling, multiple current sources conferring ability to simultaneously stimulate on multiple electrodes, and circuitry for in vivo measurement of electrode impedances. We demonstrate the performance of this device through in vitro bench-top characterization and in vivo physiological experiments with a rhesus macaque monkey.

If EMG decomposition is to be a useful tool for scientific investigation, it is essential to know that the results are accurate. Because of background noise, waveform variability, motor-unit action potential (MUAP) indistinguishability, and perplexing superpositions, accuracy assessment is not straightforward. This paper presents a rigorous statistical method for assessing decomposition accuracy based only on evidence from the signal itself. The method uses statistical decision theory in a Bayesian framework to integrate all the shape- and firing-time-related information in the signal to compute an objective a-posteriori measure of confidence in the accuracy of each discharge in the decomposition. The assessment is based on the estimated statistical properties of the MUAPs and noise and takes into account the relative likelihood of every other possible decomposition. The method was tested on 3 pairs of real EMG signals containing 4–7 active MUAP trains per signal that had been decomposed by a human expert. It rated 97% of the identified MUAP discharges as accurate to within ±0.5 ms with a confidence level of 99%, and detected 6 decomposition errors. Cross-checking between signal pairs verified all but 2 of these assertions. These results demonstrate that the approach is reliable and practical for real EMG signals.

Falls are the number one cause of injury in older adults. Wearable sensors, typically consisting of accelerometers and/or gyroscopes, represent a promising technology for preventing and mitigating the effects of falls. At present, the goal of such “ambulatory fall monitors” is to detect the occurrence of a fall and alert care providers to this event. Future systems may also provide information on the causes and circumstances of falls, to aid clinical diagnosis and targeting of interventions. As a first step towards this goal, the objective of the current study was to develop and evaluate the accuracy of a wearable sensor system for determining the causes of falls. Sixteen young adults participated in experimental trials involving falls due to slips, trips, and “other” causes of imbalance. Three-dimensional acceleration data acquired during the falling trials were input to a linear discriminant analysis technique. This routine achieved 96% sensitivity and 98% specificity in distinguishing the causes of a falls using acceleration data from three markers (left ankle, right ankle, and sternum). In contrast, a single marker provided 54% sensitivity and two markers provided 89% sensitivity. These results indicate the utility of a three-node accelerometer array for distinguishing the cause of falls.

The Neurochip-2 is a second generation, battery-powered device for neural recording and stimulating that is small enough to be carried in a chamber on a monkey’s head. It has three recording channels, with user-adjustable gains, filters, and sampling rates, that can be optimized for recording single unit activity, local field potentials, electrocorticography, electromyography, arm acceleration, etc. Recorded data are stored on a removable, flash memory card. The Neurochip-2 also has three separate stimulation channels. Two “programmable-system-on-chips” (PSoCs) control the data acquisition and stimulus output. The PSoCs permit flexible real-time processing of the recorded data, such as digital filtering and time-amplitude window discrimination. The PSoCs can be programmed to deliver stimulation contingent on neural events or deliver preprogrammed stimuli. Access pins to the microcontroller are also available to connect external devices, such as accelerometers. The Neurochip-2 can record and stimulate autonomously for up to several days in freely behaving monkeys, enabling a wide range of novel neurophysiological and neuroengineering experiments.

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.

Collaborative investigations have characterized how multineuron hippocampal ensembles encode memory necessary for subsequent successful performance by rodents in a delayed nonmatch to sample (DNMS) task and utilized that information to provide the basis for a memory prosthesis to enhance performance. By employing a unique nonlinear dynamic multi-input/multi-output (MIMO) model, developed and adapted to hippocampal neural ensemble firing patterns derived from simultaneous recorded CA1 and CA3 activity, it was possible to extract information encoded in the sample phase necessary for successful performance in the nonmatch phase of the task. The extension of this MIMO model to online delivery of electrical stimulation delivered to the same recording loci that mimicked successful CA1 firing patterns, provided the means to increase levels of performance on a trial-by-trial basis. Inclusion of several control procedures provides evidence for the specificity of effective MIMO model generated patterns of electrical stimulation. Increased utility of the MIMO model as a prosthesis device was exhibited by the demonstration of cumulative increases in DNMS task performance with repeated MIMO stimulation over many sessions on both stimulation and nonstimulation trials, suggesting overall system modification with continued exposure. Results reported here are compatible with and extend prior demonstrations and further support the candidacy of the MIMO model as an effective cortical prosthesis.

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals.

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatiotemporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the “strength” of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary “normal” encoding as a means of understanding how neural ensembles can be “tuned” to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry.

The electrolarynx (EL) is a common rehabilitative speech aid for individuals who have undergone total laryngectomy, but typically lack pitch control and require the exclusive use of one hand. The viability of using neck and face surface electromyography (sEMG) to control the onset, offset, and pitch of an EMG-controlled EL (EMG-EL) was studied. Eight individuals who had undergone total laryngectomy produced serial and running speech using a typical handheld EL and the EMG-EL while attending to real-time visual sEMG biofeedback. Running speech tokens produced with the EMG-EL were examined for naturalness by 10 listeners relative to those produced with a typical EL using a visual analog scale. Serial speech performance was assessed as the percentage of words that were fully voiced and pauses that were successfully produced. Results of the visual analog scale assessment indicated that individuals were able to use the EMG-EL without training to produce running speech perceived as natural as that produced with a typical handheld EL. All participants were able to produce running and serial speech with the EMG-EL controlled by sEMG from multiple recording locations, with the superior ventral neck or submental surface locations providing at least one of the two best control locations.

We evaluated variable patterns of pudendal nerve (PN) stimuli for reflex bladder excitation. Reflex activation of the bladder has been demonstrated previously with 20–33 Hz continuous stimulation of PN afferents. Neuronal circuits accessed by afferent mediated pathways may respond better to physiological patterned stimuli than continuous stimulation. Unilateral PN nerve cuffs were placed in neurologically intact male cats. PN stimulation (0.5–100 Hz) was performed under isovolumetric conditions at bladder volumes up to the occurrence of distension evoked reflex contractions. Stimulus evoked reflex bladder contractions were elicited in eight cats. Across all experiments, bursting of 2–10 pulses at 100–200 Hz repeated at continuous stimulation frequencies evoked significantly larger bladder responses than continuous (single pulse) stimulation (52.0 ± 44.5%). Bladder excitation was also effective at 1 Hz continuous stimuli, which is lower than typically reported. Variable patterned pulse bursting resulted in greater evoked reflex bladder pressures and increased the potential stimulation parameter space for effective bladder excitation. Improved bladder excitation should increase the efficacy of neuroprostheses for bladder control.

Neuroscience is just beginning to understand the neural computations that underlie our remarkable capacity to learn new motor tasks. Studies of natural movements have emphasized the importance of concepts such as dimensionality reduction within hierarchical levels of redundancy, optimization of behavior in the presence of sensorimotor noise and internal models for predictive control. These concepts also provide a framework for understanding the improvements in performance seen in myoelectric-controlled interface (MCI) and brain-machine interface (BMI) paradigms. Recent experiments reveal how volitional activity in the motor system combines with sensory feedback to shape neural representations and drives adaptation of behavior. By elucidating these mechanisms, a new generation of intelligent interfaces can be designed to exploit neural plasticity and restore function after neurological injury.

This paper presents the design and preliminary experimental validation of a multigrasp myoelectric controller. The described method enables direct and proportional control of multigrasp prosthetic hand motion among nine characteristic postures using two surface electromyography electrodes. To assess the efficacy of the control method, five nonamputee subjects utilized the multigrasp myoelectric controller to command the motion of a virtual prosthesis between random sequences of target hand postures in a series of experimental trials. For comparison, the same subjects also utilized a data glove, worn on their native hand, to command the motion of the virtual prosthesis for similar sequences of target postures during each trial. The time required to transition from posture to posture and the percentage of correctly completed transitions were evaluated to characterize the ability to control the virtual prosthesis using each method. The average overall transition times across all subjects were found to be 1.49 and 0.81 s for the multigrasp myoelectric controller and the native hand, respectively. The average transition completion rates for both were found to be the same (99.2%). Supplemental videos demonstrate the virtual prosthesis experiments, as well as a preliminary hardware implementation.

This paper describes a powered lower-limb orthosis that is intended to provide gait assistance to spinal cord injured (SCI) individuals by providing assistive torques at both hip and knee joints. The orthosis has a mass of 12 kg and is capable of providing maximum joint torques of 40 Nm with hip and knee joint ranges of motion from 105° flexion to 30° extension and 105° flexion to 10° hyperextension, respectively. A custom distributed embedded system controls the orthosis with power being provided by a lithium polymer battery which provides power for one hour of continuous walking. In order to demonstrate the ability of the orthosis to assist walking, the orthosis was experimentally implemented on a paraplegic subject with a T10 complete injury. Data collected during walking indicates a high degree of step-to-step repeatability of hip and knee trajectories (as enforced by the orthosis) and an average walking speed of 0.8 km/hr. The electrical power required at each hip and knee joint during gait was approximately 25 and 27 W, respectively, contributing to the 117 W overall electrical power required by the device during walking. A video of walking corresponding to the aforementioned data is included in the supplemental material.