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A Neural Recording and Stimulation Chip with Artifact Suppression for Biomedical Devices.

Xu Liu1, Juzhe Li1, Tao Chen2

  • 1College of Microelectronics, Beijing University of Technology, Ping Le Yuan 100# Chao Yang District, Beijing, China.

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This study introduces an integrated neural recording and stimulation chip with artifact suppression. The system enables closed-loop operation for biomedical devices, validated in animal experiments.

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Area of Science:

  • Biomedical Engineering
  • Neurotechnology
  • Integrated Circuit Design

Background:

  • Neural recording and stimulation are crucial for understanding brain function and developing advanced biomedical devices.
  • Artifacts caused by stimulation can interfere with neural signal quality, posing a challenge for simultaneous recording and stimulation.
  • Existing systems often lack integrated artifact suppression, limiting their real-world applicability.

Purpose of the Study:

  • To present the chip implementation of an integrated neural recording and stimulation system.
  • To incorporate a novel stimulation-induced artifact suppression technique within the system.
  • To validate the system's performance through comprehensive measurements and experiments.

Main Methods:

  • Design and fabrication of a 4-to-4 channel neural recording and stimulation chip using 0.18 µm CMOS technology.
  • Integration of low-power neural recording, stimulation, and action potential detection circuits.
  • Implementation of a closed-loop system with a proposed artifact suppression algorithm.

Main Results:

  • Successful chip implementation of the integrated neural recording and stimulation system.
  • Demonstration of effective stimulation-induced artifact suppression.
  • Validation of simultaneous neural recording and stimulation capabilities through in vivo and animal experiments.

Conclusions:

  • The developed integrated chip enables effective simultaneous neural recording and stimulation with artifact suppression.
  • The proposed artifact suppression technique enhances the reliability of neural data in closed-loop systems.
  • This technology holds significant potential for advancing closed-loop neural interfaces in biomedical applications.