A cortical-spinal prosthesis for targeted limb movement in paralyzed primate avatars

Motor paralysis is among the most disabling aspects of injury to the central nervous system. Here we develop and test a target-based cortical-spinal neural prosthesis that employs neural activity recorded from pre-motor neurons to control limb movements in functionally paralyzed primate avatars. Given the complexity by which muscle contractions are naturally controlled, we approach the problem of eliciting goal-directed limb movement in paralyzed animals by focusing on the intended targets of movement rather than their intermediate trajectories. We then match this information in real-time with spinal cord and muscle stimulation parameters that produce free planar limb movements to those intended target locations. We demonstrate that both the decoded activities of pre-motor populations and their adaptive responses can be used, after brief training, to effectively direct an avatar’s limb to distinct targets variably displayed on a screen. These findings advance the future possibility of reconstituting targeted limb movement in paralyzed subjects

A Brain-Machine Interface for Control of Medically-Induced Coma

Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.National Institutes of Health (U.S.) (Director's Transformative Award R01 GM104948)National Institutes of Health (U.S.) (Pioneer Award DP1-OD003646)National Institutes of Health (U.S.) (NIH K08-GM094394)Massachusetts General Hospital. Dept. of Anesthesia and Critical Car

Neural population partitioning and a concurrent brain-machine interface for sequential motor function

While brain-machine interfaces (BMIs) have largely focused on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence prior to its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Here, we show that concurrent BMI decoding is possible. Using population-wide modeling, we discover two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such surprising stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. Based on these findings, we develop a BMI that concurrently decodes a full motor sequence in advance of movement and then can accurately execute it as desired

Neural population partitioning and a concurrent brain-machine interface for sequential motor function

Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.National Institutes of Health (U.S.) (DP1 OD003646

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.National Institutes of Health (U.S.) (NIH grant No.DP1-0D003646-01)National Institutes of Health (U.S.) (NIH grant R01-EB006385

Confidence Prediction from EEG Recordings in a Multisensory Environment

This paper investigates the possibility of decoding decision confidence from electroencephalographic (EEG) brain activity of human subjects during a multisensory decision-making task. In recent research we have shown that decision confidence correlates could be extracted from EEG recordings during visual or auditory tasks. Here we extend these initial findings by (a) predicting the confidence in the decision from EEG recordings alone, and (b) investigating the impact of multisensory cues on decision-making behavioral data. Our results obtained from 12 participants recorded at two different sites show that the decision confidence could be predicted from EEG recordings on a single-trial basis with a mean absolute error of 0.226. Moreover, the presence of a multisensory cue did not improve the performance of the participants, but rather distracted them from the main task. Overall, these results may inform the development of cognitive systems that could monitor and alert users when they are not confident about their decisions

Parameter adaptation profiles confirm the accuracy of the calibration algorithm with continuous signals.

<p>(A–C) show sample adaptation profiles of the model parameters <b><i>ψ</i></b>_{<i>t</i>|<i>t</i>} for different learning rates <i>s</i> in ascending order. For each learning rate, the estimated parameters are within the analytically-computed 95% confidence bounds by the calibration algorithm about 96% of the time, demonstrating the accuracy of the calibration algorithm.</p

Parameter adaptation profiles confirm the accuracy of the calibration algorithm with discrete spiking activity.

<p>(A)–(C) show sample adaptation profiles of model parameters <i>ϕ</i>_{<i>t</i>|<i>t</i>} in a closed-loop BMI simulation under different learning rates <i>r</i> in ascending order. Increasing the learning rate increases the error covariance. Also, about 96% of the time, the parameter estimates at steady state are within the 95% confidence bounds computed by the calibration algorithm; this demonstrates that the calibration algorithm can closely approximate the error covariance and consequently the confidence bounds.</p

Closed-loop neural system.

<p>Closed-loop neural systems often need to learn an encoding model adaptively and in real time. The encoding model describes the relationship between neural recordings and the brain state. For example, the relevant brain state in motor BMIs is the intended velocity and in DBS systems is the disease state, e.g., in Parkinson’s disease. The neural system uses the learned encoding model to decode the brain state. This decoded brain state is then used, for example, to move a prosthetic in motor BMIs while providing visual feedback to the subject, or to control the stimulation pattern applied to the brain in DBS systems. A critical parameter for any adaptive learning algorithm is the learning rate, which dictates how fast the encoding model parameters are updated as new neural observations are received. An analytical calibration algorithm will enable achieving a predictable level of accuracy and speed in adaptive learning to improve the transient and steady-state operation of neural systems.</p