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Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.

Weijian Chen1, Xu Liu1, Peiyuan Wan1

  • 1College of Microelectronics, Beijing University of Technology, Beijing, China.

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|May 24, 2024
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Summary

This paper reviews technologies for addressing stimulation artifacts in closed-loop brain-machine interfaces (CL-BMI). It details artifact assessment, impact, and recent advancements for neural recording front-ends in neuromedicine.

Keywords:
biomedicalclosed-loop brain-machine interfacemotion artifactneural recordingstimulation artifact

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Area of Science:

  • Neuromedicine
  • Biomedical Engineering
  • Neurotechnology

Background:

  • Advancements in integrated circuits have driven progress in clinical medicine, particularly in neuromedicine.
  • Brain-machine interfaces (BMI) are revolutionizing neurological disease treatment by analyzing neural signals for therapeutic regulation.
  • Closed-loop BMI (CL-BMI) represent a significant development, offering enhanced integration and response speed.

Purpose of the Study:

  • To provide a comprehensive overview of technologies addressing stimulation artifacts (SA) in the neural recording front-end (NRFE) of CL-BMI.
  • To analyze the challenges posed by SA in CL-BMI systems.
  • To summarize and classify current and emerging artifact mitigation technologies.

Main Methods:

  • Reviewing literature on artifact assessment and impact on NRFE.
  • Exploring recent technological advancements for SA reduction in CL-BMI.
  • Classifying and analyzing the efficacy of different artifact mitigation strategies.

Main Results:

  • Stimulation artifacts pose significant challenges to NRFE in CL-BMI systems.
  • Various technologies have been developed to mitigate SA, improving neural signal quality.
  • These technologies range from hardware-based solutions to advanced signal processing algorithms.

Conclusions:

  • Addressing stimulation artifacts is crucial for the effective implementation of CL-BMI.
  • Continued research and development in artifact mitigation technologies are essential for advancing neuromedicine.
  • Future trends indicate a move towards more integrated and intelligent artifact suppression techniques.