@inproceedings{37b7f49a91f04e1ca277816df25fa271,
title = "A Fully-Integrated 1μW/Channel Dual-Mode Neural Data Acquisition System for Implantable Brain-Machine Interfaces",
abstract = "This paper presents an ultra-low power mixed-signal neural data acquisition (MSN-DAQ) system that enables a novel low-power hybrid-domain neural decoding architecture for implantable brain-machine interfaces with high channel count. Implemented in 180nm CMOS technology, the 32-channel custom chip operates at 1V supply voltage and achieves excellent performance including 1.07μW/channel, 2.37/5.62 NEF/PEF and 88dB common-mode rejection ratio (CMRR) with significant back-end power-saving advantage compared to prior works. The fabricated prototype was further evaluated with in vivo human tests at bedside, and its performance closely follows that of a commercial recording system.",
author = "Omid Malekzadeh-Arasteh and Haoran Pu and Danesh, {Ahmad Reza} and Jeffrey Lim and Wang, {Po T.} and Liu, {Charles Y.} and Do, {An H.} and Zoran Nenadic and Payam Heydari",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 ; Conference date: 01-11-2021 Through 05-11-2021",
year = "2021",
doi = "10.1109/EMBC46164.2021.9630058",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5780--5783",
booktitle = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
address = "United States",
}