This review examines design factors influencing recording stability and efficiency in brain-computer interfaces, suggesting improvements for long-term performance.
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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Chen et al. (2026) studied this question.
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