Authors: Daniel Valencia, Patrick P. Mercier and Amir Alimohammad
Publication: Journal of Neural Engineering
Date: July 22, 2022
Abstract
Objective. The ability to reliably detect neural spikes from a relatively large population of neurons contaminated with noise is imperative for reliable decoding of recorded neural information. Approach. This article first analyzes the accuracy and feasibility of various potential spike detection techniques for in vivo realizations. Then an accurate and computationally-efficient spike detection module that can autonomously adapt to variations in recording channels' statistics is presented. Main results. The accuracy of the chosen candidate spike detection technique is evaluated using both synthetic and real neural recordings. The designed detector also offers the highest decoding performance over two animal behavioral datasets among alternative detection methods. Significance. The implementation results of the designed 128-channel spike detection module in a standard 180 nm CMOS process is among the most area and power-efficient spike detection ASICs and operates within the tissue-safe constraints for brain implants, while offering adaptive noise estimation.