Material Selection and Device Design of Scalable Flexible Brain-Computer Interfaces: A Balance Between Electrical and Mechanical Performance

Brain-computer interfaces (BCIs) hold the potential to revolutionize brain function restoration, enhance human capability, and advance our understanding of cognitive mechanisms by directly linking neural signals with hardware. However, the mechanical mismatch between conventional rigid BCIs and soft brain tissue limits long-term interface stability. Next-generation BCIs must achieve long-term biocompatibility while maintaining high performance, enabling the integration of millions of sensors within tissue-level flexible and soft, stable neural interfaces. Lithographic fabrication techniques provide scalable thin-film flexible electronics, but traditional electronic materials often fail to meet the unique requirements of BCIs. This review examines the selection of materials and device design for flexible BCIs, starting with an analysis of intrinsic material properties—Young's modulus, electrical conductivity and dielectric constant. It then explores the integration of material selection with electrode design to optimize electrical circuits and assess key mechanical factors. Next, the correlation between electrical and mechanical performance is analyzed to guide material selection and device design. Finally, recent advances in neural probes are reviewed, highlighting improvements in signal quality, recording stability, and scalability. This review focuses on scalable, lithography-based BCIs, aiming to identify optimal materials and designs for long-term, reliable neural recordings.