Validation of manifold-based direct control for a brain-to-body neural bypass

Abstract

Brain-body interfaces (BBIs) are neuroprostheses that can restore the connection between brain activity and body movements. They have emerged as a radical solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands to actuate the limb from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present the design and demonstration in a monkey of a novel brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, meant to achieve ease of learning and long-term robustness. We identified once an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkey’s neural activity associated to reach-to-grasp movements. We then tested the animal’s ability to directly control a computer cursor using cortical activation along the manifold axes and demonstrated rapid learning and stable high performance over 16 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger hand movements. These results provide evidence that manifold-based direct control has promising characteristics for clinical applications of BBIs.