The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials. In a previous study , we proposed a Bayesian model coupled with a maximum a posteriori (MAP) estimator for fully automatic decomposition of multiunit recordings and we showed applications to intramuscular EMG signals. In this study, we propose a more complex signal model that includes the variability in amplitude of each unit potential. Moreover, we propose the Markov Chain Monte Carlo (MCMC) simulation and a Bayesian minimum mean square error (MMSE) estimator by averaging on samples that converge in distribution to the joint posterior law. We prove the convergence property of this approach mathematically and we test the method representatively on intramuscular multiunit recordings. The results showed that its average accuracy in spike identification is greater than 90% for intramuscular signals with up to 8 concurrently active units. In addition to intramuscular signals, the method can be applied for spike sorting of other types of multiunit recordings.
Adult; Algorithms; Bayes Theorem; Computer Simulation; Electromyography; Evoked Potentials; physiology; Humans; Male; Markov Chains; Models, Statistical; Monte Carlo Method; Muscle, Skeletal; physiology; Reproducibility of Results; Signal Processing, Computer-Assisted; instrumentation; Stochastic Processes; Young Adult; Bayesian model; MMSE estimation; Markov chain Monte Carlo; intramuscular EMG decomposition
Over the last 20 years, cochlear implants (CIs) have become what is arguably the most successful neural prosthesis to date. Despite this success, a significant number of CI recipients experience marginal hearing restoration, and, even among the best performers, restoration to normal fidelity is rare. In this article, we present image processing techniques that can be used to detect, for the first time, the positions of implanted CI electrodes and the nerves they stimulate for individual CI users. These techniques permit development of new, customized CI stimulation strategies. We present one such strategy and show that it leads to significant hearing improvement in an experiment conducted with 11 CI recipients. These results indicate that image-guidance can be used to improve hearing outcomes for many existing CI recipients without requiring additional surgical procedures.
auditory nerve; cochlear implant; image-guidance; spiral ganglion; stimulation strategy
External feedback of performance is an important component of therapy, especially for children with impairments due to cerebral palsy because they lack intrinsic experience of “good movements” to compare effort and determine performance outcomes. A robotic therapy system was developed to provide feedback for specific upper extremity movements (gestures) which are therapeutically desirable. The purpose of this study was to compare changes in forearm supination/pronation or wrist extension/flexion motion following conventional therapy and gestural robotic feedback therapy intervention. Six subjects with cerebral palsy (ages 5–18, GMFCS level IV—three subjects, level III—one subject, and level I—two subjects) participated in a blinded crossover design study of conventional and robotic feedback therapy targeting either forearm supination or wrist extension. Functional upper extremity motion at baseline and following conventional and robotic feedback therapy interventions were obtained using a motion capture system by personnel blinded to the intervention order. All activities were approved by IRB. Use of the robotic feedback system did result in slightly increased movement in the targeted gesture without change in un-targeted motions. Data also suggest a decrease in both agonist and antagonist motion following conventional therapy intervention. Results suggest improved motion when robotic feedback therapy intervention precedes conventional therapy intervention. Robotic feedback therapy is no different than conventional therapy to improve supination or wrist extension function in upper extremity impairments of children with cerebral palsy when changes were considered as aggregate data. In this very small group of diverse patients, individual subject results suggested that intervention order could be responsible for obscuring differences due to intervention type. Outcomes from several individual subjects suggest that results could be different given a more homogeneous group of subjects which future studies should be considered to ultimately determine efficacy of the robotic feedback therapy. Future studies should also address efficacy in other neuromuscular patient populations.
Cerebral palsy (CP); movement feedback; robotic feedback; upper extremity
The aims of this study are to 1) experimentally validate for the first time the nonlinear current-potential characteristics of bulk doped polycrystalline silicon in the small amplitude voltage regimes (0–200 μV) and 2) test if noise amplitudes (0–15 μV) from single neuronal electrical recordings get selectively attenuated in doped polycrystalline silicon microelectrodes due to the above property. In highly doped polycrystalline silicon, bulk resistances of several hundred kilo-ohms were experimentally measured for voltages typical of noise amplitudes and 9–10 kΩ for voltages typical of neural signal amplitudes (>150–200 μV). Acute multiunit measurements and noise measurements were made in n = 6 and n = 8 anesthetized adult rats, respectively, using polycrystalline silicon and tungsten microelectrodes. There was no significant difference in the peak-to-peak amplitudes of action potentials recorded from either microelectrode (p > 0.10). However, noise power in the recordings from tungsten microelectrodes (26.36 ± 10.13 pW) was significantly higher (p < 0.001) than the corresponding value in polycrystalline silicon microelectrodes (7.49 ± 2.66 pW). We conclude that polycrystalline silicon microelectrodes result in selective attenuation of noise power in electrical recordings compared to tungsten microelectrodes. This reduction in noise compared to tungsten microelectrodes is likely due to the exponentially higher bulk resistances offered by highly doped bulk polycrystalline silicon in the range of voltages corresponding to noise in multiunit measurements.
Brain implants; brain prosthesis; brain–machine interface; neural prostheses
This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of 9 subjects with incomplete cervical SCI while they tried to perform 6 different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (>97%) were achieved in classification of 7 different classes (6 intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.
Surface EMG; myoelectic control; pattern recognition; spinal cord injury
We have previously reported on a novel variable impedance knee mechanism (VIKM). The VIKM was designed as a component of a hybrid neuroprosthesis to regulate knee flexion. The hybrid neuroprosthesis is a device that uses a controllable brace to support the body against collapse while stimulation provides power for movement. The hybrid neuroprosthesis requires a control system to coordinate the actions of the VIKM with the stimulation system; the development and evaluation of such a controller is presented. Brace mounted sensors and a baseline open loop stimulation pattern are utilized as control signals to activate the VIKM during stance phase while simultaneously modulating muscle stimulation in an on-off fashion. The objective is twofold: reduce the amount of stimulation necessary for walking while simultaneously restoring more biologically correct knee motion during stance using the VIKM. Custom designed hardware and software components were developed for controller implementation. The VIKM hybrid neuroprosthesis (VIKM-HNP) was evaluated during walking in one participant with thoracic level spinal cord injury. In comparison to walking with functional neuromuscular stimulation (FNS) alone, the VIKM-HNP restored near normal stance phase knee flexion during loading response and pre-swing phases while decreasing knee extensor stimulation by up to 40%.
Functional neuromuscular stimulation (FNS); Hybrid neuroprosthesis (HNP); Controllable Orthosis; Spinal Cord Injury; Gait
Children with autism spectrum disorder (ASD) demonstrate potent impairments in social communication skills including atypical viewing patterns during social interactions. Recently, several assistive technologies, particularly virtual reality (VR), have been investigated to address specific social deficits in this population. Some studies have coupled eye-gaze monitoring mechanisms to design intervention strategies. However, presently available systems are designed to primarily chain learning via aspects of one’s performance only which affords restricted range of individualization. The presented work seeks to bridge this gap by developing a novel VR-based interactive system with Gaze-sensitive adaptive response technology that can seamlessly integrate VR-based tasks with eye-tracking techniques to intelligently facilitate engagement in tasks relevant to advancing social communication skills. Specifically, such a system is capable of objectively identifying and quantifying one’s engagement level by measuring real-time viewing patterns, subtle changes in eye physiological responses, as well as performance metrics in order to adaptively respond in an individualized manner to foster improved social communication skills among the participants. The developed system was tested through a usability study with eight adolescents with ASD. The results indicate the potential of the system to promote improved social task performance along with socially-appropriate mechanisms during VR-based social conversation tasks.
Autism spectrum disorder (ASD); blink rate (BR); eye-tracking; fixation duration (FD); pupil diameter (PD); virtual reality (VR)
Emerging technology, especially robotic technology, has been shown to be appealing to children with autism spectrum disorders (ASD). Such interest may be leveraged to provide repeatable, accurate and individualized intervention services to young children with ASD based on quantitative metrics. However, existing robot-mediated systems tend to have limited adaptive capability that may impact individualization. Our current work seeks to bridge this gap by developing an adaptive and individualized robot-mediated technology for children with ASD. The system is composed of a humanoid robot with its vision augmented by a network of cameras for real-time head tracking using a distributed architecture. Based on the cues from the child’s head movement, the robot intelligently adapts itself in an individualized manner to generate prompts and reinforcements with potential to promote skills in the ASD core deficit area of early social orienting. The system was validated for feasibility, accuracy, and performance. Results from a pilot usability study involving six children with ASD and a control group of six typically developing (TD) children are presented.
Rehabilitation robotics; robot and autism; robotassisted autism intervention; social human–robot interaction
Impairments in social communication skills are thought to be core deficits in children with autism spectrum disorder (ASD). In recent years, several assistive technologies, particularly Virtual Reality (VR), have been investigated to promote social interactions in this population. It is well known that children with ASD demonstrate atypical viewing patterns during social interactions and thus monitoring eye-gaze can be valuable to design intervention strategies. While several studies have used eye-tracking technology to monitor eye-gaze for offline analysis, there exists no real-time system that can monitor eye-gaze dynamically and provide individualized feedback. Given the promise of VR-based social interaction and the usefulness of monitoring eye-gaze in real-time, a novel VR-based dynamic eye-tracking system is developed in this work. This system, called Virtual Interactive system with Gaze-sensitive Adaptive Response Technology (VIGART), is capable of delivering individualized feedback based on a child’s dynamic gaze patterns during VR-based interaction. Results from a usability study with six adolescents with ASD are presented that examines the acceptability and usefulness of VIGART. The results in terms of improvement in behavioral viewing and changes in relevant eye physiological indexes of participants while interacting with VIGART indicate the potential of this novel technology.
Autism spectrum disorder (ASD); blink rate; eyetracking; fixation duration; pupil diameter; virtual reality (VR)
Functional recovery is typically poor after facial nerve transection and surgical repair. In rats, whisking amplitude remains greatly diminished after facial nerve regeneration, but can recover more completely if the whiskers are periodically mechanically stimulated during recovery. Here we present a robotic “whisk assist” system for mechanically driving whisker movement after facial nerve injury. Movement patterns were either pre-programmed to reflect natural amplitudes and frequencies, or movements of the contralateral (healthy) side of the face were detected and used to control real-time mirror-like motion on the denervated side. In a pilot study, twenty rats were divided into nine groups and administered one of eight different whisk assist driving patterns (or control) for 5–20 minutes, five days per week, across eight weeks of recovery after unilateral facial nerve cut and suture repair. All rats tolerated the mechanical stimulation well. Seven of the eight treatment groups recovered average whisking amplitudes that exceeded controls, although small group sizes precluded statistical confirmation of group differences. The potential to substantially improve facial nerve recovery through mechanical stimulation has important clinical implications, and we have developed a system to control the pattern and dose of stimulation in the rat facial nerve model.
facial nerve; facial paralysis; nerve regeneration; rehabilitation; reinnervation; robotic; vibrissae; whisking
P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.
Brain-computer interface; dynamic stopping; P300 speller
We investigated neural effects of visuo-motor discordances during visually-guided finger movements. An fMRI-compatible data glove was used to actuate (in real-time) virtual hand models shown on a display in 1st person perspective. In experiment 1, we manipulated virtual hand motion to simulate either hypometric or unintentional (actuation of a mismatched finger) feedback of sequential finger flexion in healthy subjects. Analysis of finger motion revealed no significant differences in movement behavior across conditions, suggesting that between-condition differences in brain activity could only be attributed to varying modes of visual feedback rather than motor output. Activation in the veridical relative to either altered feedback conditions was localized to the ipsilateral motor cortex. Hypometric feedback and mismatched finger feedback (relative to veridical) were associated with distinct activation. Hypometric feedback was associated with activation in the contralateral motor cortex. Mismatched feedback was associated with activation in bilateral ventral premotor, left dorsal premotor and left occipitotemporal cortex. The time it took the subject to evaluate visuomotor discordance was positively correlated with activation in bilateral supplementary motor area, bilateral insula, right postcentral gyrus, bilateral dorsal premotor areas and bilateral posterior parietal lobe. In Experiment 2, we investigated the effects of hypo- and hypermetric visual feedback in three stroke subjects. We observed increased activation of ipsilesional motor cortex in both hypometric and hypermetric feedback conditions. Our data suggest that manipulation of visual feedback of one’s own hand movement may be used to facilitate activity in select brain networks. We suggest that these effects can be exploited in neurorehabilition to enhance the processes of brain reorganization after injury and, specifically, might be useful in aiding recovery of hand function in patients during virtual reality-based training.
motor control; fMRI; virtual reality; action observation; visuomotor
The unilateral 6-hydroxydopamine (6-OHDA) lesioned rat model is frequently used to study the effects of subthalamic nucleus (STN) deep brain stimulation (DBS) for the treatment of Parkinson’s disease. However, systematic knowledge of the effects of DBS parameters on behavior in this animal model is lacking. The goal of this study was to characterize the effects of DBS on methamphetamine-induced circling in the unilateral 6-OHDA lesioned rat. DBS parameters tested include stimulation amplitude, stimulation frequency, methamphetamine dose, stimulation polarity, and anatomical location of the electrode. When an appropriate stimulation amplitude and dose of methamphetamine were applied, high frequency stimulation (> 130 Hz), but not low frequency stimulation (< 10 Hz), reversed the bias in ipsilateral circling without inhibiting movement. This characteristic frequency tuning profile was only generated when at least one electrode used during bipolar stimulation was located within the STN. No difference was found between bipolar stimulation and monopolar stimulation when the most effective electrode contact was selected, indicating that monopolar stimulation could be used in future experiments. Methamphetamine-induced circling is a simple, reliable, and sensitive behavioral test and holds potential for high-throughput study of the effects of STN DBS in unilaterally lesioned rats.
STN DBS; Parkinson’s disease; 6-OHDA
Epilepsy affects approximately one percent of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback.
brain stimulation; electrographic seizure feedback control; transcranial focal stimulation; tripolar concentric ring electrodes; seizure detection
A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulse-rates. In studying the neural coding of limb-state in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches.
dorsal root ganglia; multi-electrode array; neural coding; neural prostheses; sensory cortex; sensory feedback
Electrical stimulation of nervous tissue has been extensively used as both a tool in experimental neuroscience research and as a method for restoring of neural functions in patients suffering from sensory and motor disabilities. In the central nervous system, intracortical microstimulation (ICMS) has been shown to be an effective method for inducing or biasing perception, including visual and tactile sensation. ICMS also holds promise for enabling brain-machine-brain interfaces (BMBIs) by directly writing information into the brain. Here we detail the design of a high-side, digitally current-controlled biphasic, bipolar microstimulator, and describe the validation of the device in vivo. As many applications of this technique, including BMBIs, require recording as well as stimulation, we pay careful attention to isolation of the stimulus channels and parasitic current injection. With the realized device and standard recording hardware - without active artifact rejection - we are able to observe stimulus artifacts of less than 2 ms in duration.
microstimulation; cortex; artifact; suppression
Motor unit number index (MUNIX) measurement has recently achieved increasing attention as a tool to evaluate the progression of motoneuron diseases. In our current study, the sensitivity of the MUNIX technique to changes in motoneuron and muscle properties was explored by a simulation approach utilizing variations on published motoneuron pool and surface electromyogram (EMG) models. Our simulation results indicate that, when keeping motoneuron pool and muscle parameters unchanged and varying the input motor unit numbers to the model, then MUNIX estimates can appropriately characterize changes in motor unit numbers. Such MUNIX estimates are not sensitive to different motor unit recruitment and rate coding strategies used in the model. Furthermore, alterations in motor unit control properties do not have a significant effect on the MUNIX estimates. Neither adjustment of the motor unit recruitment range nor reduction of the motor unit firing rates jeopardizes the MUNIX estimates. The MUNIX estimates closely correlate with the maximum M wave amplitude. However, if we reduce the amplitude of each motor unit action potential rather than simply reduce motor unit number, then MUNIX estimates substantially underestimate the motor unit numbers in the muscle. These findings suggest that the current MUNIX definition is most suitable for motoneuron diseases that demonstrate secondary evidence of muscle fiber reinnervation. In this regard, when MUNIX is applied, it is of much importance to examine a parallel measurement of motor unit size index (MUSIX), defined as the ratio of the maximum M wave amplitude to the MUNIX. However, there are potential limitations in the application of the MUNIX methods in atrophied muscle, where it is unclear whether the atrophy is accompanied by loss of motor units or loss of muscle fiber size.
EMG; motor unit index; M wave; simulation
The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 Spinal Cord Injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from “voluntary” activations with less than a 3.6% RMS prediction error.
Functional Electrical Stimulation (FES); Neural prostheses; Musculoskeletal modeling; Spinal cord injury (SCI)
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.
bidirectional interface; brain-machine interface; intracortical microstimulation; neural prosthesis
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.
Brain computer interfaces; connectivity analysis; motor control; time-varying dynamic Bayesian networks
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.
Frequency shift keying; implantable microelectronic devices; neural interfacing; pulse width modulation; telemetry; time division multiplexing
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.
Elbow spasticity; haptic simulation; inter-rater reliability; modified Ashworth scale; spasticity assessment
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.
Functional Neuromuscular Stimulation; Rehabilitation; Spinal Cord Injury; Standing; Control System
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.
Beamforming; blind source separation; cuff electrode; flat interface nerve electrode; inverse problem; localization; selective neural recording; spatial filters