A Fully-Integrated 1μW/Channel Dual-Mode Neural Data Acquisition System for Implantable Brain-Machine Interfaces

Omid Malekzadeh-Arasteh, Haoran Pu, Ahmad Reza Danesh, Jeffrey Lim, Po T. Wang, Charles Y. Liu, An H. Do, Zoran Nenadic, Payam Heydari

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

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.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint

Dive into the research topics of 'A Fully-Integrated 1μW/Channel Dual-Mode Neural Data Acquisition System for Implantable Brain-Machine Interfaces'. Together they form a unique fingerprint.

Cite this