Design choices for next-generation neurotechnology can impact motion artifact in electrophysiological and fast-scan cyclic voltammetry measurements

Evan N. Nicolai, Nicholas J. Michelson, Megan L. Settell, Seth A. Hara, James K. Trevathan, Anders J. Asp, Kaylene C. Stocking, J. Luis Lujan, Takashi D.Y. Kozai, Kip A. Ludwig

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Implantable devices to measure neurochemical or electrical activity from the brain are mainstays of neuroscience research and have become increasingly utilized as enabling components of clinical therapies. In order to increase the number of recording channels on these devices while minimizing the immune response, flexible electrodes under 10 μm in diameter have been proposed as ideal next-generation neural interfaces. However, the representation of motion artifact during neurochemical or electrophysiological recordings using ultra-small, flexible electrodes remains unexplored. In this short communication, we characterize motion artifact generated by the movement of 7 μm diameter carbon fiber electrodes during electrophysiological recordings and fast-scan cyclic voltammetry (FSCV) measurements of electroactive neurochemicals. Through in vitro and in vivo experiments, we demonstrate that artifact induced by motion can be problematic to distinguish from the characteristic signals associated with recorded action potentials or neurochemical measurements. These results underscore that new electrode materials and recording paradigms can alter the representation of common sources of artifact in vivo and therefore must be carefully characterized.

Original languageEnglish (US)
Article number494
JournalMicromachines
Volume9
Issue number10
DOIs
StatePublished - 2018

Keywords

  • Artifact
  • Brain-machine interfaces
  • Electrochemistry
  • Electrode
  • Electrophysiology
  • Fast-scan cyclic voltammetry (FSCV)
  • Neural interface
  • Neuromodulation
  • Neuroprosthetics
  • Neurotechnology

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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