What question did this study set out to answer?

This review aims to analyze the impact of materials and architectures on the performance of brain-computer interfaces.

July 1, 2026npj Biosensing1 citationsOpen Access

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

QCQi ChenFudan University NHNingge HuangFudan University CTChuanjie TongInstitute of Microelectronics

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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