Creating new functional circuits for action via brain-machine interfaces

Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses

Subject-specific modulation of local field potential spectral power during brain–machine interface control in primates

Objective. Intracortical brain–machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control. Approach. We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0–150 Hz range. Main results. While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0–80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80–150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0–40, 40–80, and 80–150 Hz) of the 0–150 Hz band. Interestingly, although both monkeys performed better with some sub-ranges than others, they were able to achieve BMI control with all sub-ranges after decoder adaptation, demonstrating broad flexibility in the frequencies that could potentially be used for LFP-based BMI control. Significance. Overall, our results demonstrate proficient, continuous BMI control using LFPs and provide insight into the subject-specific spectral patterns of LFP activity modulated during control

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

<div><p>Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.</p></div

Performance over the process of adaptive OFC-PPF.

<p>(A) Success rate as a function of time from the start of the experiment in one session. Success rate is calculated in sliding 2 min windows. The decoder was initialized using a visual feedback seed. Adaptive OFC-PPF was then run as described in the flow-chart in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004730#pcbi.1004730.g002" target="_blank">Fig 2</a>. The architecture started by providing assisted training and adaptation. After the first assist period, which consisted of 3 discrete assist levels, performance in the test period exceeded the desired threshold of 5 trials/min and hence the architecture stopped the assisted training (vertical dashed black line). We stopped the adaptation at the vertical dashed blue line, after which the trained point process model was used in a random-walk PPF to control the cursor. Green lines show the 99% upper bound on the chance level performance during the assisted training. Assisted performance is above the 99% chance level. The horizontal dashed line shows the mean manual task performance with the arm on that day. (B) Randomly selected center-out trials on this day after adaptation stopped.</p

Adaptive OFC-PPF extends to tasks beyond those used for CLDA training.

<p>(A) Sample random trajectories in the target-jump task. Gray circle shows the initial target and cyan circle shows the eventual target after the jump occurred. The unfilled circle on the trajectory shows the time at which the jump occurred. The monkey used a random-walk PPF trained on the center-out task to perform this target-jump task. (B) Sample random trajectories in the target-to-target task. Each trial type consisted of a start target and an end target. Instead of going from the center to one of eight peripheral targets in the center-out task, here the monkey had to move the cursor from one target to another target (whose locations could also differ from those in the center-out task).</p

Adaptive OFC-PPF BMI architecture.

<p>(A) Monkey performing the self-paced delayed center-out movement task in brain control. The subject’s arms were confined within a primate chair in brain control. (B) Timeline of the center-out task (see Experimental Procedures for details). (C) Adaptive OFC-PPF converts the spiking activity into a discrete time-series of 0’s and 1’s by binning the spikes in small intervals containing at most one spike; this binary time-series is modeled as a point process. We thus perform the decoding and parameter adaptation with every binary spike event. (D) Adaptive OFC-PPF architecture. The architecture models the brain in closed-loop BMI control as an infinite-horizon optimal feedback-controller to infer its intended velocity during adaptation. The inputs to the infinite-horizon optimal feedback-controller model are the visual feedback of the decoded cursor kinematics (that the monkey observes) and the instructed target position (i.e., task goal). The inferred intended velocity is input to a point process filter for each neuron, which estimates the neuron’s parameters with every 0 and 1 spike event. These estimated parameters are used in the kinematic PPF decoder that decodes the kinematics with every 0 and 1 spike event. Initially, the architecture can provide assisted training to the subject by decoding the kinematics using a target-directed PPF decoder (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004730#sec002" target="_blank">Methods</a>). After assisted training is complete, a random-walk PPF is used to decode the kinematics. Once performance converges, adaptation stops and the trained random-walk PPF is used by the monkey to perform various BMI tasks, such as the center-out or the target-jump tasks.</p

Spike-event-based adaptation enables faster convergence.

<p>(A) Performance over time for adaptive OFC-PPF (solid) and SmoothBatch OFC-PPF (dashed) run on two sets (red and blue) of two consecutive days that started from the same initial parameters. Vertical lines show the time point where assistance stopped as the subject’s non-assisted success rate in the test period at that point exceeded the desired minimum threshold of 5 trials/min. Success rate is calculated in sliding 2 min windows. (B, C) Average success rate across sessions as a function of time into the adaptive session for SmoothBatch OFC-PPF in (B) and Adaptive OFC-PPF in (C). Blue curves show the mean success rate over 12 days of experiments for each decoder and shading reflects the standard deviation across these days. The red bar shows the time range in which the BMI architecture stopped the assisted training across days. Spike-event-based adaptation resulted in faster convergence and less variability compared with SmoothBatch adaptation that updated the decoder parameters on a slower adaptation time-scale, i.e., once every 90 seconds.</p

Adaptive OFC-PPF is robust to initialization.

<p>(A) Performance over time for adaptive OFC-PPF that was initialized once using a visual feedback seed and once using a permuted visual feedback seed on the same day. Vertical dashed lines show the time point at which the architecture stopped the assisted training. Regardless of the initial seed, performance converges to similar values in these two sessions. Note that initial performance of both visual feedback and permuted visual feedback seeds were poor and hence assistance was used to allow the subject perform the task initially as parameters were adapting. (B–D) Convergence of the point process parameters for an example neuron as a function of time, when starting from the two different seeds. The baseline firing rate is shown in (B) and <i>α</i>^<i>c</i> for the velocity components in the two dimensions are shown in (C) and (D) (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004730#pcbi.1004730.e009" target="_blank">Eq (5)</a>).</p