This paper focuses on how AI large models, such as Transformers and meta-learning can empower brain-computer interface (BCI) chips to achieve dynamic adaptation, thereby overcoming the limitations of traditional fixed decoding models that struggle to adapt to individual neural plasticity and dynamic changes in brain states. It analyzes pathways to enhance chip generalization and real-time performance across three technical dimensions: hardware architecture, algorithm optimization, and multimodal fusion. The paper also explores core challenges like data privacy and energy-efficiency tradeoffs. Building on this foundation, it proposes a neuromorphic computing design framework for next-generation chips to advance the intelligent and personalized development of BCI in medical rehabilitation and human-computer interaction.