Intracortical Somatosensory Stimulation to Elicit Fingertip Sensations in an Individual With Spinal Cord Injury.

BACKGROUND AND OBJECTIVES: The restoration of touch to fingers and fingertips is critical to achieving dexterous neuroprosthetic control for individuals with sensorimotor dysfunction. However, localized fingertip sensations have not been evoked via intracortical microstimulation (ICMS). METHODS: Using a novel intraoperative mapping approach, we implanted electrode arrays in the finger areas of left and right somatosensory cortex and delivered ICMS over a 2-year period in a human participant with spinal cord injury. RESULTS: Stimulation evoked tactile sensations in 8 fingers, including fingertips, spanning both hands. Evoked percepts followed expected somatotopic arrangements. The subject was able to reliably identify up to 7 finger-specific sites spanning both hands in a finger discrimination task. The size of the evoked percepts was on average 33% larger than a finger pad, as assessed via manual markings of a hand image. The size of the evoked percepts increased modestly with increased stimulation intensity, growing 21% as pulse amplitude increased from 20 to 80 µA. Detection thresholds were estimated on a subset of electrodes, with estimates of 9.2 to 35 µA observed, roughly consistent with prior studies. DISCUSSION: These results suggest that ICMS can enable the delivery of consistent and localized fingertip sensations during object manipulation by neuroprostheses for individuals with somatosensory deficits. CLINICALTRIALSGOV IDENTIFIER: NCT03161067.

Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques.

Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.

Novel intraoperative online functional mapping of somatosensory finger representations for targeted stimulating electrode placement: technical note.

Defining eloquent cortex intraoperatively, traditionally performed by neurosurgeons to preserve patient function, can now help target electrode implantation for restoring function. Brain-machine interfaces (BMIs) have the potential to restore upper-limb motor control to paralyzed patients but require accurate placement of recording and stimulating electrodes to enable functional control of a prosthetic limb. Beyond motor decoding from recording arrays, precise placement of stimulating electrodes in cortical areas associated with finger and fingertip sensations allows for the delivery of sensory feedback that could improve dexterous control of prosthetic hands. In this study, the authors demonstrated the use of a novel intraoperative online functional mapping (OFM) technique with high-density electrocorticography to localize finger representations in human primary somatosensory cortex. In conjunction with traditional pre- and intraoperative targeting approaches, this technique enabled accurate implantation of stimulating microelectrodes, which was confirmed by postimplantation intracortical stimulation of finger and fingertip sensations. This work demonstrates the utility of intraoperative OFM and will inform future studies of closed-loop BMIs in humans.

Common marmoset (Callithrix jacchus) as a primate model for behavioral neuroscience studies.

BACKGROUND: The common marmoset (Callithrix jacchus) has been proposed as a suitable bridge between rodents and larger primates. They have been used in several types of research including auditory, vocal, visual, pharmacological and genetics studies. However, marmosets have not been used as much for behavioral studies. NEW METHOD: Here we present data from training 12 adult marmosets for behavioral neuroscience studies. We discuss the husbandry, food preferences, handling, acclimation to laboratory environments and neurosurgical techniques. In this paper, we also present a custom built "scoop" and a monkey chair suitable for training of these animals. RESULTS: The animals were trained for three tasks: 4 target center-out reaching task, reaching tasks that involved controlling robot actions, and touch screen task. All animals learned the center-out reaching task within 1-2 weeks whereas learning reaching tasks controlling robot actions task took several months of behavioral training where the monkeys learned to associate robot actions with food rewards. COMPARISON TO EXISTING METHOD: We propose the marmoset as a novel model for behavioral neuroscience research as an alternate for larger primate models. This is due to the ease of handling, quick reproduction, available neuroanatomy, sensorimotor system similar to larger primates and humans, and a lissencephalic brain that can enable implantation of microelectrode arrays relatively easier at various cortical locations compared to larger primates. CONCLUSION: All animals were able to learn behavioral tasks well and we present the marmosets as an alternate model for simple behavioral neuroscience tasks.

Flight simulation using a Brain-Computer Interface: A pilot, pilot study.

As Brain-Computer Interface (BCI) systems advance for uses such as robotic arm control it is postulated that the control paradigms could apply to other scenarios, such as control of video games, wheelchair movement or even flight. The purpose of this pilot study was to determine whether our BCI system, which involves decoding the signals of two 96-microelectrode arrays implanted into the motor cortex of a subject, could also be used to control an aircraft in a flight simulator environment. The study involved six sessions in which various parameters were modified in order to achieve the best flight control, including plane type, view, control paradigm, gains, and limits. Successful flight was determined qualitatively by evaluating the subject's ability to perform requested maneuvers, maintain flight paths, and avoid control losses such as dives, spins and crashes. By the end of the study, it was found that the subject could successfully control an aircraft. The subject could use both the jet and propeller plane with different views, adopting an intuitive control paradigm. From the subject's perspective, this was one of the most exciting and entertaining experiments she had performed in two years of research. In conclusion, this study provides a proof-of-concept that traditional motor cortex signals combined with a decoding paradigm can be used to control systems besides a robotic arm for which the decoder was developed. Aside from possible functional benefits, it also shows the potential for a new recreational activity for individuals with disabilities who are able to master BCI control.

Classification of pilot-induced oscillations during in-flight piloting exercises using dry EEG sensor recordings.

Pilot-Induced Oscillations (PIOs) are potentially hazardous piloting phenomena in which a pilot's control-inputs and the aircraft control-responses have (for any of a number of possible reasons) become out of phase. During PIOs, aggressive over-controlling on the part of the pilot in order to overcome a perceived lack of control can lead to complete loss of aircraft control. This study shows data recorded from a Cognionics dry electrode system during actual flight exercises can be used on a second-to-second basis to classify whether a pilot was undergoing a PIO event or if a PIO was imminent. If such PIO predictions could be made with adequate accuracy and robustness in real-time, they could form the basis of systems aimed at detecting and/or mitigating PIOs.

Kernel temporal differences for neural decoding.

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces.

Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

OBJECTIVE: Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. APPROACH: Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. MAIN RESULTS: The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. SIGNIFICANCE: By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI.

New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.

A new method of concurrently visualizing states, values, and actions in reinforcement based brain machine interfaces.

This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.

The effects of targeted muscle reinnervation on neuromas in a rabbit rectus abdominis flap model.

PURPOSE: A targeted muscle reinnervation (TMR) model was created using a pedicled rabbit rectus abdominis (RA) flap to receive the input from previously amputated forelimb neuromas. We hypothesize that a segmental muscle flap can undergo TMR and that it is possible to differentiate the signal from 3 independent nerves. In addition, by virtue of the nerve coaptation, the morphology of the previous amputation neuroma would become more like that of an in-continuity neuroma. METHODS: Five New Zealand white rabbits had a forelimb amputation. In a second-stage surgery, an RA flap was transposed onto the chest wall. After neuroma excision, 3 neurorrhaphies were made between the median nerve, radial nerve, and ulnar nerves, and 3 motor nerves of the RA. After 10 weeks, the electrophysiologic properties of the reinnervated flap were tested. Nerve specimens from the median, radial, and ulnar nerves were harvested before and after TMR to quantify the histomorphometric changes effected by TMR on the mixed nerve neuromas. RESULTS: Of the 12 nerve coaptations performed in the 4 viable flaps, all 12 were grossly successful. Muscle surface EMG data demonstrated that the RA retained its segmental innervation pattern after TMR. Similarly, prolonged stimulation of 1 nerve reinnervating the RA resulted in the depletion of glycogen specific to the territory of the muscle stimulated by that nerve. TMR was found to favorably alter the histomorphometric characteristics of the neuroma by decreasing myelinated fiber counts and increasing fascicle diameter in the transferred nerves. CONCLUSIONS: This study demonstrates that 1 segmented muscle having TMR by multiple nerve ingrowth and in turn generate discrete EMG signals. During this process, the previous amputation neuroma undergoes favorable morphologic alteration. CLINICAL RELEVANCE: Based on these preclinical results, this technique might be useful in upper extremity amputees to recruit target muscles to have reinnervation to drive myoelectric prostheses and to treat symptomatic neuromas.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Closing the loop in cortically-coupled computer vision: a brain-computer interface for searching image databases.

We describe a closed-loop brain-computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of target detection, attentional shifts and self-monitoring processes, which result from the user paying attention to target images interspersed in rapid serial visual presentation (RSVP) sequences. The highest scored images are passed to a semi-supervised computer vision system that reorganizes the image database accordingly, using a graph-based representation that captures visual similarity between images. The system can either query the user for more information, by adaptively resampling the database to create additional RSVP sequences, or it can converge to a 'done' state. The done state includes a final ranking of the image database and also a 'guess' of the user's chosen category of interest. We find that the closed-loop system's re-rankings can substantially expedite database searches for target image categories chosen by the subjects. Furthermore, better reorganizations are achieved than by relying on EEG interest rankings alone, or if the system were simply run in an open loop format without adaptive resampling.

Combining computer and human vision into a BCI: can the whole be greater than the sum of its parts?

Our group has been investigating the development of BCI systems for improving information delivery to a user, specifically systems for triaging image content based on what captures a user's attention. One of the systems we have developed uses single-trial EEG scores as noisy labels for a computer vision image retrieval system. In this paper we investigate how the noisy nature of the EEG-derived labels affects the resulting accuracy of the computer vision system. Specifically, we consider how the precision of the EEG scores affects the resulting precision of images retrieved by a graph-based transductive learning model designed to propagate image class labels based on image feature similarity and sparse labels.

Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles.

Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation using simple control signals, such as those derived from residual movement or muscle activity. However, the use of such fixed stimulation patterns limits hand function to the few tasks programmed into the controller. In contrast, we are developing a system that uses neural signals recorded from a multi-electrode array implanted in the motor cortex; this system has the potential to provide independent control of multiple muscles over a broad range of functional tasks. Two monkeys were able to use this cortically controlled FES system to control the contraction of four forearm muscles despite temporary limb paralysis. The amount of wrist force the monkeys were able to produce in a one-dimensional force tracking task was significantly increased. Furthermore, the monkeys were able to control the magnitude and time course of the force with sufficient accuracy to track visually displayed force targets at speeds reduced by only one-third to one-half of normal. Although these results were achieved by controlling only four muscles, there is no fundamental reason why the same methods could not be scaled up to control a larger number of muscles. We believe these results provide an important proof of concept that brain-controlled FES prostheses could ultimately be of great benefit to paralyzed patients with injuries in the mid-cervical spinal cord.

A fully implanted drug delivery system for peripheral nerve blocks in behaving animals.

Inhibiting peripheral nerve function can be useful for many studies of the nervous system or motor control. Accomplishing this in a temporary fashion in animal models by using peripheral nerve blocks permits studies of the immediate effects of the loss, and/or any resulting short-term changes and adaptations in behavior or motor control, while avoiding the complications commonly associated with permanent lesions, such as sores or self-mutilation. We have developed a method of quickly and repeatedly inducing temporary, controlled motor deficits in rhesus macaque monkeys via a chronically implanted drug delivery system. This assembly consists of a nerve cuff and a subdermal injection dome, and has proved effective for delivering local anesthetics directly to peripheral nerves for many months. Using this assembly for median and ulnar nerve blocks routinely resulted in over 80% losses in hand and wrist strength for rhesus monkeys. The assembly was also effective for inducing ambulatory motor deficits in rabbits through blocks of the sciatic nerve. Interestingly, while standard anesthetics were sufficient for the rabbit nerve blocks, the inclusion of epinephrine was essential for achieving significant motor blockade in the monkeys.

Control of muscle synergies by cortical ensembles.

Since its introduction in the early 1980s, the concept of a "preferred direction" for neuronal discharge has proven to be a powerful means of studying diverse properties of individual neurons in the motor areas of the brain. More recently, the activity recorded from ensembles of neurons, each with an identified preferred direction, has been used to predict hand movement, both off-line, and in real-time. Our recent experiments have addressed similar issues, but have focused on the relation between primary motor cortical discharge and muscle activity, rather than limb kinematics. We recently introduced the concept of a "muscle-space" preferred direction (PD(M)), that is analogous to the familiar hand-space preferred direction (PD(H)). In this manuscript, we show that there is considerable variety in the direction of these PD(M) vectors across neurons, but that for a given task and neuron, two successive measurements of PD(M) are very similar. We found that these vectors tend to form clusters in particular regions of the muscle space that may reflect neurons that control synergistically important groups of muscles. We have also shown that the discharge measured from neural ensembles can be used to predict the activity of individual muscles, in much the way that kinematic signals have been predicted by other groups. In fact, the accuracy of these predictions is similar to that of kinematic signals, despite the stochastic nature and greater bandwidth of the EMG signals. PD(M)s represent a divergence from one neuron to numerous muscles, while the prediction of muscle activity represents convergence from many neurons to individual muscles. We are continuing to investigate the nature of this complex matrix of functional interconnections.

Prediction of upper limb muscle activity from motor cortical discharge during reaching.

Movement representation by the motor cortex (M1) has been a theoretical interest for many years, but in the past several years it has become a more practical question, with the advent of the brain-machine interface. An increasing number of groups have demonstrated the ability to predict a variety of kinematic signals on the basis of M1 recordings and to use these predictions to control the movement of a cursor or robotic limb. We, on the other hand, have undertaken the prediction of myoelectric (EMG) signals recorded from various muscles of the arm and hand during button pressing and prehension movements. We have shown that these signals can be predicted with accuracy that is similar to that of kinematic signals, despite their stochastic nature and greater bandwidth. The predictions were made using a subset of 12 or 16 neural signals selected in the order of each signal's unique, output-related information content. The accuracy of the resultant predictions remained stable through a typical experimental session. Accuracy remained above 80% of its initial level for most muscles even across periods as long as two weeks. We are exploring the use of these predictions as control signals for neuromuscular electrical stimulation in quadriplegic patients.

Biomimetic brain machine interfaces for the control of movement.

Quite recently, it has become possible to use signals recorded simultaneously from large numbers of cortical neurons for real-time control. Such brain machine interfaces (BMIs) have allowed animal subjects and human patients to control the position of a computer cursor or robotic limb under the guidance of visual feedback. Although impressive, such approaches essentially ignore the dynamics of the musculoskeletal system, and they lack potentially critical somatosensory feedback. In this mini-symposium, we will initiate a discussion of systems that more nearly mimic the control of natural limb movement. The work that we will describe is based on fundamental observations of sensorimotor physiology that have inspired novel BMI approaches. We will focus on what we consider to be three of the most important new directions for BMI development related to the control of movement. (1) We will present alternative methods for building decoders, including structured, nonlinear models, the explicit incorporation of limb state information, and novel approaches to the development of decoders for paralyzed subjects unable to generate an output signal. (2) We will describe the real-time prediction of dynamical signals, including joint torque, force, and EMG, and the real-time control of physical plants with dynamics like that of the real limb. (3) We will discuss critical factors that must be considered to incorporate somatosensory feedback to the BMI user, including its potential benefits, the differing representations of sensation and perception across cortical areas, and the changes in the cortical representation of tactile events after spinal injury.