Miniature Untethered EEG Recorder Improves Advanced Neuroscience Methodologies

Christopher Crispin-Bailey* (Corresponding Author), Jim Austin, Bettina Platt* (Corresponding Author), Anthony Moulds, Barry Crouch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
13 Downloads (Pure)

Abstract

Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model. Epochs capturing specific behaviors were automatically identified and extracted prior to further analysis. This was achieved by the novel design of a ultraminiature wearable EEG recorder, the NAT-1 device, and its insitu IR recording module. The device is described in detail, and its contribution to enabling new neuroscience is demonstrated.
Original languageEnglish
Pages (from-to)1101-1111
Number of pages11
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume13
Issue number5
Early online date14 Aug 2019
DOIs
Publication statusPublished - Oct 2019

Bibliographical note

NAT-1 was developed at the University of York. We wish to acknowledge the contributions made by Mike Freeman on early firmware, and Tom Lampert’s contributions to early evaluation studies [6].
The IRDB module and NAT-1 devices were developed by Anthony Moulds. The NAT-1 device, and IR-Board were subsequently commercialized under license to Cybula Ltd.
We particularly wish to thank Professor Gernot Riedel, University of Aberdeen, for his surgical support and expertise within the clinical studies referenced in this paper.

Keywords

  • Experimental neuroscience
  • EEG recording
  • low-power sensors

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