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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Qi Chen,
  • Ningge Huang,
  • Chuanjie Tong,
  • Pengchuan Liu,
  • Shaojun Que,
  • Huize Xue,
  • Xiao Xiao,
  • Enming Song

摘要

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.