A 1.5µW NEO-based Spike Detector with Adaptive-Threshold for Calibration-free Multichannel Neural Interfaces

Abstract—This paper presents a novel front-end circuit for detecting action potentials in extracellular neural recordings. By implementing a real-time, adaptive algorithm to determine an effective threshold for robustly detecting a spike, the need for calibration and/or external monitoring is eliminated. The input signal is first pre-processed by utilising a non-linear energy operator (NEO) to effectively boost the signal-to-noise ratio (SNR) of the spike feature of interest. The spike detection threshold is then determined by tracking the peak NEO response and applying a non-linear gain to realise an adaptive response to different spike amplitudes and background noise levels. The proposed algorithm and its implementation is shown to achieve both accurate and robust spike detection, by minimising falsely detected spikes and/or missed spikes. The system has been implemented in a commercially available 0.18µm technology requiring a total power consumption of 1.5µW from a 1.8 V supply and occupying a compact footprint of only 0.03 mm2 silicon area. The proposed circuit is thus ideally suited for high-channel count, calibration-free, neural interfaces. I