Visual evoked potentials determine chronic signal quality in a stent-electrode endovascular neural interface

G Gerboni, S E John, G S Rind, S M Ronayne, C N May, T J Oxley, D B Grayden, N L Opie, Y T Wong

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Brain-machine interfaces directly communicate between the brain and external devices. An effective trade-off between signal quality and safety required for successful clinical translation has yet to be achieved, and represents a great challenge for engineers, neuroscientists, and clinicians. The StentrodeTM, an endovascular neural interface that allows neural signals to be recorded from within a cortical vessel, has demonstrated feasibility with safety for up to six months in a large animal model. Easily-obtained electrophysiological signals to evaluate the effect of implant duration on recording performance are essential for ongoing evaluation of the device. In this work, we demonstrate that a non-invasive and quick visual stimulation technique can be used to assess chronic signal quality of an endovascular neural interface in awake freely moving animals. Visual stimulation requires little or no training to elicit a large, consistent, and well-characterized response. We report the stability of recording quality with the Stentrode over 30 days using voltage measures of signal-to-noise ratio (18.78 ± 1.92 dB, mean ± std) and peak-to-peak voltages (56.07 ± 9.56 μV) computed on visual evoked responses. Signal amplitude and electrochemical impedance spectroscopy suggest that stabilization of the electrode-tissue interface occurred over the first 20 days. However, during this stabilization period, recording quality was minimally impacted.

Original languageEnglish
Article number055018
Number of pages14
JournalBiomedical Physics and Engineering Express
Volume4
Issue number5
DOIs
Publication statusPublished - 20 Aug 2018

Keywords

  • brain-machine interface
  • endovascular electrodes
  • neural interfaces
  • neural signals
  • visual evoked potentials

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