Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays

Sepideh Rastegar; Justin Stadlbauer; Kiyo Fujimoto; Kari McLaughlin; David Estrada; Kurtis D. Cantley

doi:10.1109/MWSCAS.2017.8052971

Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays

Sepideh Rastegar
, Justin Stadlbauer
, Kiyo Fujimoto
, Kari McLaughlin
, David Estrada
, Kurtis D. Cantley

Boise State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

9 Downloads (Pure)

Abstract

This work is aimed toward the goal of investigating the influence of different materials on the signal-to-noise ratio (SNR) of passive neural microelectrode arrays (MEAs). Noise reduction is one factor that can substantially improve neural interface performance. The MEAs are fabricated using gold, indium tin oxide (ITO), and chemical vapor deposited (CVD) graphene. 3D-printed Nylon reservoirs are then adhered to the glass substrates with identical MEA patterns. Reservoirs are filled equally with a fluid that is commonly used for neuronal cell culture. Signals are applied to glass micropipettes immersed in the solution, and response is measured on an oscilloscope from a microprobe placed on the contact pad external to the reservoir. The time domain response signal is transformed into a frequency spectrum, and SNR is calculated from the ratio of power spectral density of the signal to the power spectral density of baseline noise at the frequency of the applied signal. We observed as the magnitude or the frequency of the input voltage signal gets larger, graphene-based MEAs increase the signal-to-noise ratio significantly compared to MEAs made of ITO and gold. This result indicates that graphene provides a better interface with the electrolyte solution and could lead to better performance in neural hybrid systems for in vitro investigations of neural processes.

Original language	American English
Title of host publication	2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	507-510
Number of pages	4
ISBN (Electronic)	9781509063895
DOIs	https://doi.org/10.1109/MWSCAS.2017.8052971
State	Published - 2017
Event	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States Duration: 6 Aug 2017 → 9 Aug 2017

Publication series

Name	Midwest Symposium on Circuits and Systems
Volume	2017-August
ISSN (Print)	1548-3746

Conference

Conference	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Country/Territory	United States
City	Boston
Period	6/08/17 → 9/08/17

Keywords

CVD graphene
Microelectrode Arrays
Neural interface
Noise power spectrum
Signal-to-noise ratio

EGS Disciplines

Materials Science and Engineering

Access to Document

10.1109/MWSCAS.2017.8052971

Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive MiAccepted author manuscript, 377 KBLicense: Unspecified

Cite this

Rastegar, S., Stadlbauer, J., Fujimoto, K., McLaughlin, K., Estrada, D., & Cantley, K. D. (2017). Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 507-510). Article 8052971 (Midwest Symposium on Circuits and Systems; Vol. 2017-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS.2017.8052971

@inproceedings{53a554562d83454da03c6a56a599a1f0,

title = "Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays",

abstract = "This work is aimed toward the goal of investigating the influence of different materials on the signal-to-noise ratio (SNR) of passive neural microelectrode arrays (MEAs). Noise reduction is one factor that can substantially improve neural interface performance. The MEAs are fabricated using gold, indium tin oxide (ITO), and chemical vapor deposited (CVD) graphene. 3D-printed Nylon reservoirs are then adhered to the glass substrates with identical MEA patterns. Reservoirs are filled equally with a fluid that is commonly used for neuronal cell culture. Signals are applied to glass micropipettes immersed in the solution, and response is measured on an oscilloscope from a microprobe placed on the contact pad external to the reservoir. The time domain response signal is transformed into a frequency spectrum, and SNR is calculated from the ratio of power spectral density of the signal to the power spectral density of baseline noise at the frequency of the applied signal. We observed as the magnitude or the frequency of the input voltage signal gets larger, graphene-based MEAs increase the signal-to-noise ratio significantly compared to MEAs made of ITO and gold. This result indicates that graphene provides a better interface with the electrolyte solution and could lead to better performance in neural hybrid systems for in vitro investigations of neural processes.",

keywords = "CVD graphene, Microelectrode Arrays, Neural interface, Noise power spectrum, Signal-to-noise ratio",

author = "Sepideh Rastegar and Justin Stadlbauer and Kiyo Fujimoto and Kari McLaughlin and David Estrada and Cantley, \{Kurtis D.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 ; Conference date: 06-08-2017 Through 09-08-2017",

year = "2017",

doi = "10.1109/MWSCAS.2017.8052971",

language = "American English",

series = "Midwest Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)",

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Rastegar, S, Stadlbauer, J, Fujimoto, K, McLaughlin, K, Estrada, D & Cantley, KD 2017, Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays. in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)., 8052971, Midwest Symposium on Circuits and Systems, vol. 2017-August, Institute of Electrical and Electronics Engineers Inc., pp. 507-510, 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017, Boston, United States, 6/08/17. https://doi.org/10.1109/MWSCAS.2017.8052971

Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays. / Rastegar, Sepideh; Stadlbauer, Justin; Fujimoto, Kiyo et al.
2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). Institute of Electrical and Electronics Engineers Inc., 2017. p. 507-510 8052971 (Midwest Symposium on Circuits and Systems; Vol. 2017-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays

AU - Rastegar, Sepideh

AU - Stadlbauer, Justin

AU - Fujimoto, Kiyo

AU - McLaughlin, Kari

AU - Estrada, David

AU - Cantley, Kurtis D.

PY - 2017

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N2 - This work is aimed toward the goal of investigating the influence of different materials on the signal-to-noise ratio (SNR) of passive neural microelectrode arrays (MEAs). Noise reduction is one factor that can substantially improve neural interface performance. The MEAs are fabricated using gold, indium tin oxide (ITO), and chemical vapor deposited (CVD) graphene. 3D-printed Nylon reservoirs are then adhered to the glass substrates with identical MEA patterns. Reservoirs are filled equally with a fluid that is commonly used for neuronal cell culture. Signals are applied to glass micropipettes immersed in the solution, and response is measured on an oscilloscope from a microprobe placed on the contact pad external to the reservoir. The time domain response signal is transformed into a frequency spectrum, and SNR is calculated from the ratio of power spectral density of the signal to the power spectral density of baseline noise at the frequency of the applied signal. We observed as the magnitude or the frequency of the input voltage signal gets larger, graphene-based MEAs increase the signal-to-noise ratio significantly compared to MEAs made of ITO and gold. This result indicates that graphene provides a better interface with the electrolyte solution and could lead to better performance in neural hybrid systems for in vitro investigations of neural processes.

AB - This work is aimed toward the goal of investigating the influence of different materials on the signal-to-noise ratio (SNR) of passive neural microelectrode arrays (MEAs). Noise reduction is one factor that can substantially improve neural interface performance. The MEAs are fabricated using gold, indium tin oxide (ITO), and chemical vapor deposited (CVD) graphene. 3D-printed Nylon reservoirs are then adhered to the glass substrates with identical MEA patterns. Reservoirs are filled equally with a fluid that is commonly used for neuronal cell culture. Signals are applied to glass micropipettes immersed in the solution, and response is measured on an oscilloscope from a microprobe placed on the contact pad external to the reservoir. The time domain response signal is transformed into a frequency spectrum, and SNR is calculated from the ratio of power spectral density of the signal to the power spectral density of baseline noise at the frequency of the applied signal. We observed as the magnitude or the frequency of the input voltage signal gets larger, graphene-based MEAs increase the signal-to-noise ratio significantly compared to MEAs made of ITO and gold. This result indicates that graphene provides a better interface with the electrolyte solution and could lead to better performance in neural hybrid systems for in vitro investigations of neural processes.

KW - CVD graphene

KW - Microelectrode Arrays

KW - Neural interface

KW - Noise power spectrum

KW - Signal-to-noise ratio

UR - https://www.scopus.com/pages/publications/85034105622

UR - https://scholarworks.boisestate.edu/mse_facpubs/324

U2 - 10.1109/MWSCAS.2017.8052971

DO - 10.1109/MWSCAS.2017.8052971

M3 - Conference contribution

T3 - Midwest Symposium on Circuits and Systems

SP - 507

EP - 510

BT - 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017

Y2 - 6 August 2017 through 9 August 2017

ER -

Rastegar S, Stadlbauer J, Fujimoto K, McLaughlin K, Estrada D , Cantley KD. Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). Institute of Electrical and Electronics Engineers Inc. 2017. p. 507-510. 8052971. (Midwest Symposium on Circuits and Systems). doi: 10.1109/MWSCAS.2017.8052971

Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays

Abstract

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Keywords

EGS Disciplines

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