From deep brain stimulation to brain–computer interfaces: current progress in implantable neurotechnology
TL;DR
It is argued that the same handful of bottlenecks recur across all three technologies (long-term stability, neural coding, and equitable access) and the governance frameworks needed alongside continued engineering progress are outlined.
Abstract
Brain implants, including deep brain stimulation (DBS) systems, brain–computer interfaces (BCIs), and speech neuroprostheses, are moving from the proof-of-concept stage to early clinical deployment. Although these systems target different clinical problems, we argue that they share a common closed-loop architecture (sensing, decoding, stimulation or output, power and telemetry, and chronic clinical validation) and that their translational pace is set by bottlenecks at these shared stages rather than by challenges unique to each technology. Recent milestones, including the regulatory clearance of a cortical interface and an expanding set of implanted-BCI trials, have accelerated progress in the field. This mini-review uses this shared architecture to compare progress across three fronts: the shift from open-to-closed-loop DBS and its widening range of neurological and psychiatric indications; BCIs restoring motor and sensory function; and speech neuroprostheses that decode attempted speech into text, voice, and facial animation. We highlight enabling advances in flexible electrodes, AI-assisted decoding, and neuromorphic edge processing, and examine unresolved controversies over electrode architecture and system design. We conclude that the same handful of bottlenecks recur across all three technologies (long-term stability, neural coding, and equitable access) and outline the governance frameworks needed alongside continued engineering progress.