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A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Authors :
Wu J
Akinin A
Somayajulu J
Lee MS
Paul A
Lu H
Park Y
Kim SJ
Mercier PP
Cauwenberghs G
Source :
IEEE transactions on biomedical circuits and systems [IEEE Trans Biomed Circuits Syst] 2024 Apr; Vol. 18 (2), pp. 263-273. Date of Electronic Publication: 2024 Apr 02.
Publication Year :
2024

Abstract

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V <subscript>rms</subscript> input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV <subscript>rms</subscript> over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

Details

Language :
English
ISSN :
1940-9990
Volume :
18
Issue :
2
Database :
MEDLINE
Journal :
IEEE transactions on biomedical circuits and systems
Publication Type :
Academic Journal
Accession number :
38408002
Full Text :
https://doi.org/10.1109/TBCAS.2024.3368068