The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements. In the application of BMI to paralysis or amputation, arm movements are not feasible, and imagined movements create weaker, partially unrelated patterns of neural activity. BMI training must begin naive, without access to these prototypical methods for parameter initialization used in most laboratory BMI demonstrations. Naive adaptive BMI refer to a class of methods recently introduced to address this problem. We first identify the basic elements of existing approaches based on adaptive filtering and define a decoder, ReFIT-PPF to represent these existing approaches. We then present Joint RSE, a novel approach that logically extends prior approaches. Using recently developed human- and synthetic-subjects closed-loop BMI simulation platforms, we show that Joint RSE significantly outperforms ReFIT-PPF and nonadaptive (static) decoders. Control experiments demonstrate the critical role of jointly estimating neural parameters and user intent. In addition, we show that nonzero sensorimotor delay in the user significantly degrades ReFIT-PPF but not Joint RSE, owing to differences in the prior on intended velocity. Paradoxically, substantial differences in the nature of sensory feedback between these methods do not contribute to differences in performance between Joint RSE and ReFIT-PPF. Instead, BMI performance improvement is driven by machine learning, which outpaces rates of human learning in the human-subjects simulation platform. In this regime, nuances of error-related feedback to the human user are less relevant to rapid BMI mastery.

The closed-loop operation of brain-machine interfaces (BMI) provides a framework to study the mechanisms behind neural control through a restricted output channel, with emerging clinical applications to stroke, degenerative disease, and trauma. Despite significant empirically driven improvements in closed-loop BMI systems, a fundamental, experimentally validated theory of closed-loop BMI operation is lacking. Here we propose a compact model based on stochastic optimal control to describe the brain in skillfully operating canonical decoding algorithms. The model produces goal-directed BMI movements with sensory feedback and intrinsically noisy neural output signals. Various experimentally validated phenomena emerge naturally from this model, including performance deterioration with bin width, compensation of biased decoders, and shifts in tuning curves between arm control and BMI control. Analysis of the model provides insight into possible mechanisms underlying these behaviors, with testable predictions. Spike binning may erode performance in part from intrinsic control-dependent constraints, regardless of decoding accuracy. In compensating decoder bias, the brain may incur an energetic cost associated with action potential production. Tuning curve shifts, seen after the mastery of a BMI-based skill, may reflect the brain's implementation of a new closed-loop control policy. The direction and magnitude of tuning curve shifts may be altered by decoder structure, ensemble size, and the costs of closed-loop control. Looking forward, the model provides a framework for the design and simulated testing of an emerging class of BMI algorithms that seek to directly exploit the presence of a human in the loop.

The execution of reaching movements involves the coordinated activity of multiple brain regions that relate variously to the desired target and a path of arm states to achieve that target. These arm states may represent positions, velocities, torques, or other quantities. Estimation has been previously applied to neural activity in reconstructing the target separately from the path. However, the target and path are not independent. Because arm movements are limited by finite muscle contractility, knowledge of the target constrains the path of states that leads to the target. In this letter, we derive and illustrate a state equation to capture this basic dependency between target and path. The solution is described for discrete-time linear systems and gaussian increments with known target arrival time. The resulting analysis enables the use of estimation to study how brain regions that relate variously to target and path together specify a trajectory. The corresponding reconstruction procedure may also be useful in brain-driven prosthetic devices to generate control signals for goal-directed movements.