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July 1, 2026npj Biosensing1 citationsOpen Access

Materials and microsystems for stable and high-efficiency brain-computer interfaces in signal recording

QCQi ChenNHNingge HuangCTChuanjie Tong

Key Points

  • This review aims to analyze the impact of materials and architectures on the performance of brain-computer interfaces.
  • Reviewed degradation patterns in neural recording systems.
  • Identified strategies for enhancing long-term performance.
  • Outlined design principles for scalable neural interfaces.
  • Improved understanding of factors affecting stability and efficiency.
  • Proposed new strategies to enhance the durability of neural interfaces.
  • Highlighted recent advancements in integrated systems for better performance.

Abstract

Brain–computer interfaces enable high-resolution neural recording, yet chronic performance is constrained by interacting factors across materials, electrode and array architectures, and microsystem integration. This review examines how these cross-level design layers collectively shape recording stability, efficiency, and scalability. We summarize representative degradation patterns, strategies for improving long-term performance, and recent advances in integrated systems, and outline design principles for clinically scalable and reliable neural interfaces.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a44add75cd2549c8bc43490https://doi.org/10.1038/s44328-026-00109-7
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