A Low-Power Wireless 128-Channel Neural Interface Circuit for Multiregional Brain Recording

This paper proposes a low-power, wireless 128channel neural interface circuit, comprising a 40 nm recorder circuit and a WiFi module. The proposed design enables realtime monitoring and neural signal decoding during behavioral studies, facilitating high-density, long-term tracking of neural signals in freely moving subjects. The recorder circuit integrates 16 -bit resolution at a maximum rate of 32 k samples per second (sps), with a power consumption of 3.43 mW and an area of $6.27 \mathrm{~mm}^{2}$,achieving the lowest normalized power consumption of $0.84 \mu \mathrm{~W} / \mathrm{ch} / \mathrm{ksps}$ among state-of-the-art works. The VoltageControlled Oscillator (VCO) Analog Front Ends (AFEs) feature an input-referred noise of $0.66 \mu \mathrm{~V}_{\text {rms}}$ within the $0.5-60 \mathrm{~Hz}$ range. The digital backend allows flexible channel configuration and includes an on-chip CIC filter to reduce out-of-band noise. The WiFi module, connected via QSPI, facilitates wireless control and data transmission. The proposed circuit was validated through electrocorticogram (ECoG) measurements in rodent subjects, demonstrating its reliability and flexibility.

Authors

Institutions

Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/biocas61083.2024.10798169
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Low-Power Wireless 128-Channel Neural Interface Circuit for Multiregional Brain Recording

Yahao Song, Yuwei Zhang, Tianyi Liu, Milin Zhang et al.
Neuroscience and Neural Engineering
article

A Low-Power Wireless 128-Channel Neural Interface Circuit for Multiregional Brain Recording

Yahao Song, Yuwei Zhang, Tianyi Liu, Milin Zhang, Minqian Zheng, Chao Sun
article en

Abstract

This paper proposes a low-power, wireless 128channel neural interface circuit, comprising a 40 nm recorder circuit and a WiFi module. The proposed design enables realtime monitoring and neural signal decoding during behavioral studies, facilitating high-density, long-term tracking of neural signals in freely moving subjects. The recorder circuit integrates 16 -bit resolution at a maximum rate of 32 k samples per second (sps), with a power consumption of 3.43 mW and an area of $6.27 \mathrm{~mm}^{2}$,achieving the lowest normalized power consumption of $0.84 \mu \mathrm{~W} / \mathrm{ch} / \mathrm{ksps}$ among state-of-the-art works. The VoltageControlled Oscillator (VCO) Analog Front Ends (AFEs) feature an input-referred noise of $0.66 \mu \mathrm{~V}_{\text {rms}}$ within the $0.5-60 \mathrm{~Hz}$ range. The digital backend allows flexible channel configuration and includes an on-chip CIC filter to reduce out-of-band noise. The WiFi module, connected via QSPI, facilitates wireless control and data transmission. The proposed circuit was validated through electrocorticogram (ECoG) measurements in rodent subjects, demonstrating its reliability and flexibility.

Tsinghua University (CN)
Research and Development
Openalex Percentile: Top 74%
Neuroscience and Neural Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Low-Power Wireless 128-Channel Neural Interface Circuit for Multiregional Brain Recording — Yahao Song, Yuwei Zhang, et al. · (2024) | TGRS Research Map | TGRS