Ultra-low-power high sensitivity spike detectors based on modified nonlinear energy operator

Spike detectors are important data-compression components for state-of-the-art implantable neural recording microsystems. This paper proposes two improved spike detection algorithms, frequency-enhanced nonlinear energy operator (fNEO) and energy-of-derivative (ED), to solve the sensitivity reduction of a conventional nonlinear energy operator (NEO) in the presence of baseline interference. The proposed methods are implemented in two analog spike detectors with a standard 0.13-μm CMOS process. To achieve an ultra-low-power design, weak-inversion MOSFET based multipliers, adders and derivative circuits are developed to work with a 0.5 V power supply. The power dissipations of the proposed fNEO spike detector and the ED spike detector are 258.7 nW and 129.4 nW, respectively. Quantitative investigations based on the standard deviation and peak-to-clutter ratio of the detected spikes indicate that the proposed spike detector schemes hold higher sensitivity than the conventional NEO based spike detector.

I. Introduction

Multichannel simultaneous recordings of neural action potentials (APs) facilitate the studies in neuroprosthetics. State-of-the-art implantable neural recording microsystems can achieve real-time wireless monitoring of APs over 100 channels. However, increasing the number of channels gives rise to a larger data volume, which requires higher data rate and higher bandwidth for data transmission. The power dissipation of wireless transmission block is therefore increased dramatically. While a sampling frequency of 24 KHz per channel and the number of bits per sample is 10, a 100-channel microsystem will require a transmission bandwidth of 24 Mbps. It is far beyond the existing telemetry link limit of about 1–2 Mbps for biomedical applications [1].