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Frequency-Division Multiplexing with Graphene Active Electrodes for Neurosensor Applications.

Jinyong Kim1, Carly V Fengel2, Siyuan Yu1

  • 1School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331 USA.

IEEE Transactions on Circuits and Systems. II, Express Briefs : a Publication of the IEEE Circuits and Systems Society
|May 21, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces graphene active electrodes for neural recording, reducing wires by enabling signal sharing via frequency-division multiplexing (FDM). This innovation enhances neural interface technology for better neuron interaction studies.

Keywords:
frequency division multiplexing (FDM)graphenemulti-channelneural-recording

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Area of Science:

  • Neuroscience
  • Materials Science
  • Electrical Engineering

Background:

  • Multielectrode arrays are standard for neural recording, typically requiring one wire per electrode.
  • Conventional architectures limit the number of recording sites due to wiring complexity.

Purpose of the Study:

  • To develop a novel N-wire, N-electrode array architecture for neural recording.
  • To reduce the number of access wires while maintaining high signal quality and density.

Main Methods:

  • Utilized graphene active electrodes for signal upconversion at the recording site.
  • Implemented frequency-division multiplexing (FDM) to share interface wires among multiple electrodes.
  • Designed and fabricated a custom integrated circuit (IC) analog front-end (AFE) in 0.18 μm CMOS for signal modulation and readout.
  • Employed digital demodulation for signal recovery.

Main Results:

  • Demonstrated successful signal modulation using graphene field-effect transistor (GFET) electrodes.
  • Achieved multi-channel FDM with integrated circuit (IC) and digital signal processing (DSP) demodulation.
  • Provided electrical characterization of the developed system.

Conclusions:

  • The proposed approach effectively breaks the N-wire, N-electrode limitation in neural recording.
  • This technology enables high signal count, high spatial resolution, and high temporal precision for neural interaction studies.
  • Significant reduction in access wires is achieved, paving the way for more advanced neural interfaces.