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. Signal is 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.
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. doi: 10.1109/MWSCAS.2017.8052971
Rastegar, Sepideh; Stadlbauer, Justin; Fujimoto, Kiyo; McLaughlin, Kari; Estrada, David; and Cantley, Kurtis D.. (2017). "Signal-to-Noise Ratio Enhancement Using Graphene-Based Passive Microelectrode Arrays". 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 507-510. http://dx.doi.org/10.1109/MWSCAS.2017.8052971