NeuroBMI: A New Neuromorphic Implantable Wireless Brain Machine Interface with A 0.48 µW Event-Driven Noise-Tolerant Spike Detector

Abstract

The use of Brain-Machine Interfaces (BMIs) in neuroscience research and neural prosthetics has seen widespread application. With the technology trend shifting from wearable to implantable wireless BMIs featuring increasing channel counts, the volume of data generated requires impractically high bandwidth and transmission power for the implants. In this paper, we present NeuroBMI, a novel neuromorphic implantable wireless BMI that leverages a unified neuromorphic strategy for neural signal sampling, processing, and transmission. The proposed NeuroBMI and neuromorphic strategy reduces transmitted data rate and overall power consumption. NeuroBMI takes into account the high sparsity of neural signals by employing an integrateand-fire sampling based analog-to-spike converter (ASC), which generates digital spike trains based on triggered events and avoids unnecessary data sampling. Additionally, an event-driven noise-tolerant spike detector and event-driven spike transmitter are also proposed, to further reduce the energy consumption and transmitted data rate. Simulation results demonstrate that the proposed NeuroBMI achieves a data compression ratio of 520, with the proposed spike detector consuming only 0.48 µW.