An implantable, rechargeable neuromodulation research tool using a distributed interface and algorithm architecture

Implantable medical devices can provide chronic access to the nervous system. Implants containing embedded scientific instrumentation payloads (e.g. - sensors, classification, and control policy implementation) provide a unique opportunity for exploring diseased neural networks and how these neural networks may be better treated. Physically embedding payloads in an implant creates intertwined constraints such as power consumption, algorithmic computation limits and lack of flexibility, data storage, and the scale of sensing information. These limitations can be largely addressed with a combination of rechargeable batteries and high-bandwidth, secure, distance telemetry, which enables a distributed neural research system. Taking advantage of a distributed architecture helps facilitate scientific investigation in a more unconstrained environment. In this paper, we describe the design of an implantable research tool, discuss the prototype system architecture and its design details, and present preliminary bench verification and validation with human data drawn from representative use cases.