A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System With In-Pixel Digitization and Crosstalk Compensation

Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, inter-modal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180 nm CMOS technology, occupies a total area of 33 mm<sup>2</sup> with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150 $\mu$m $\times$ 130 $\mu$m. The total system power consumption is 19.68 mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The in-vitro experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Biomedical Circuits and Systems
Published
2024-09-13
DOI
https://doi.org/10.1109/tbcas.2024.3460388
Citations
2
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System With In-Pixel Digitization and Crosstalk Compensation

Yue Cao, Songyu Han, Timothy G. Constandinou, Lin Yang et al.
2 citations
IEEE Transactions on Biomedical Circuits and Systems
EEG and Brain-Computer Interfaces
0.69
article

A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System With In-Pixel Digitization and Crosstalk Compensation

Yue Cao, Songyu Han, Timothy G. Constandinou, Lin Yang, Xinguo Wang, Guoxing Wang, Xing-Lei Song, Yan Liu, Peng Yan, Lei Qian, Chen Wang, Pujia Xing
article en
2 citations

Abstract

Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, inter-modal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180 nm CMOS technology, occupies a total area of 33 mm<sup>2</sup> with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150 $\mu$m $\times$ 130 $\mu$m. The total system power consumption is 19.68 mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The in-vitro experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.

IEEE Transactions on Biomedical Circuits and SystemsVol. 19(3)
Shanghai Jiao Tong University (CN), ETH Zurich (CH), UK Dementia Research Institute (GB), Imperial College London (GB), KU Leuven (BE)
Openalex Percentile: Top 31%
EEG and Brain-Computer Interfaces
0.69
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.