Direct Cortical Control of 3D Neuroprosthetic Devices

TLDR

Neural activity can drive 3D neuroprosthetic movement when decoded in real time, yet prior work assumed fixed neuron tuning and did not provide subjects with awareness of predicted movements. The study provided subjects with real‑time visual feedback of their brain‑controlled trajectories. Cell tuning adapted during brain‑controlled movement, enabling long 3D sequences with fewer cortical units when algorithms tracked these changes, and daily practice further improved accuracy and tuning.

Abstract

Three-dimensional (3D) movement of neuroprosthetic devices can be controlled by the activity of cortical neurons when appropriate algorithms are used to decode intended movement in real time. Previous studies assumed that neurons maintain fixed tuning properties, and the studies used subjects who were unaware of the movements predicted by their recorded units. In this study, subjects had real-time visual feedback of their brain-controlled trajectories. Cell tuning properties changed when used for brain-controlled movements. By using control algorithms that track these changes, subjects made long sequences of 3D movements using far fewer cortical units than expected. Daily practice improved movement accuracy and the directional tuning of these units.