Brain-computer interface （BCI） establishes a mapping relationship between external stimuli and internal neural activity in the brain， providing an effective means to understand brain information processing mechanisms and achieve human-machine intelligent interaction. In recent years， foundational models have achieved breakthrough progress in various computer vision tasks， which has also propelled BCIs from task-specific models toward a general intelligence new paradigm. This paper is the first to review the latest research advances of foundational models in neural encoding and decoding for BCIs. It systematically outlines key studies and research trajectories in natural stimulus encoding-decoding， multimodal brain representation learning， and generalization studies. The analysis identifies current challenges in sample size， data heterogeneity， multimodal fusion， and model interpretability. Finally， it highlights future research directions for general-purpose BCIs. This work aims to provide a systematic reference and research insights for building general BCI models capable of handling complex cognitive scenarios.