A Clockless Robust Bionic Spike Detector for Implantable Brain-Computer Interfaces

The application of Brain-Computer Interfaces (BCIs) in neuroscience and neurological disorders has witnessed extensive adoption. With the shift towards implantable wireless BCIs featuring higher channel counts, the surge in data volume presents challenges in bandwidth and transmission power. On-implant feature extraction and spike detection are crucial to reduce data bandwidth, but suffers from limited power bud-gets. Optimizing power consumption while maintaining detection accuracy are thus vital for efficient BCI systems. In this paper, we present a clockless robust bionic low-power spike detector for implantable BCI. The proposed spike detector achieves low-power and event-driven clockless bionic detection by inter-module co-design that integrates pipelined analog-to-spike conversion sampling, neural signal feature extraction, and 2-stage depolarization and repolarization based spike detection. Moreover, a sampling-aware unsupervised firing-rate-based detection threshold search method is proposed to achieve robust and adaptive detection. Simulation results demonstrate that the proposed spike detector achieves 90.9% - 100% average detection accuracy on a commonly used synthetic dataset with only 0.95 μW power consumption per channel.