Skip to main content

Research Repository

Advanced Search

Electrode fabrication and interface optimization for imaging of evoked peripheral nervous system activity with electrical impedance tomography (EIT)

Chapman, C A R; Aristovich, K; Donega, M; Fjordbakk, C T; Stathopoulou, T-R; Viscasillas, J; Avery, J; Perkins, J D M; Holder, D

Authors

C A R Chapman

K Aristovich

M Donega

C T Fjordbakk

T-R Stathopoulou

J Viscasillas

J Avery

J D M Perkins

D Holder



Abstract

Objective. Non-invasive imaging techniques are undoubtedly the ideal methods for continuous monitoring of neural activity. One such method, fast neural electrical impedance tomography (EIT) has been developed over the past decade in order to image neural action potentials with non-penetrating electrode arrays. Approach. The goal of this study is two-fold. First, we present a detailed fabrication method for silicone-based multiple electrode arrays which can be used for epicortical or neural cuff applications. Secondly, we optimize electrode material coatings in order to achieve the best accuracy in EIT reconstructions. Main results. The testing of nanostructured electrode interface materials consisting of platinum, iridium oxide, and PEDOT:pTS in saline tank experiments demonstrated that the PEDOT:pTS coating used in this study leads to more accurate reconstruction dimensions along with reduced phase separation between recording channels. The PEDOT:pTS electrodes were then used in vivo to successfully image and localize the evoked activity of the recurrent laryngeal fascicle from within the cervical vagus nerve. Significance. These results alongside the simple fabrication method presented here position EIT as an effective method to image neural activity.

Citation

Chapman, C. A. R., Aristovich, K., Donega, M., Fjordbakk, C. T., Stathopoulou, T., Viscasillas, J., …Holder, D. (2019). Electrode fabrication and interface optimization for imaging of evoked peripheral nervous system activity with electrical impedance tomography (EIT). Journal of Neural Engineering, 16, 016001. https://doi.org/10.1088/1741-2552/aae868

Journal Article Type Article
Acceptance Date Oct 15, 2018
Publication Date Jan 1, 2019
Deposit Date Mar 22, 2019
Publicly Available Date Mar 29, 2024
Journal Journal of Neural Engineering
Print ISSN 1741-2560
Publisher IOP Publishing
Peer Reviewed Peer Reviewed
Volume 16
Pages 016001
DOI https://doi.org/10.1088/1741-2552/aae868
Public URL https://rvc-repository.worktribe.com/output/1384204

Files




You might also like



Downloadable Citations