Abstract
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.