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

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.-R., Viscasillas, J., Avery, J., Perkins, J. D. M., & 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 22, 2019
• Journal: Journal of Neural Engineering
• Print ISSN: 1741-2560
• Electronic ISSN: 1741-2552
• 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