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
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.
High-frequency alternating current; nerve conduction block; nerve cuff electrode; onset response; peripheral nerve
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.
Bipolar; depolarization; electrode; high frequency; nerve block; nerve cuff; peripheral nerve
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.
neural engineering; semicircular canal implant; sensory neural prosthesis
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.
electromyography; EMG decomposition; motor units; Bayesian analysis; a-posteriori probability
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.
PMID: 21859608 CAMSID: cams2291
Accelerometers; aging; balance; biomechanics; fall detection; falls; injury; linear discriminant analysis (LDA); machine learning; postural stability
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.
Brain–computer interface (BCI); neural recording; neural stimulation; primate