Brain-Computer Interface (BCI) establishes information pathways between the human brain and external devices by analyzing brain activity, providing new technical means for assisting communication, functional assessment, rehabilitation training, and neural regulation. Electroencephalography (EEG) offers advantages such as non-invasiveness, low cost, high temporal resolution, and ease of repeated acquisition, making it one of the most widely used signal modalities in current non-invasive BCI and brain health research. In recent years, artificial intelligence has driven EEG decoding from classical control command recognition to gradually expand to complex brain state representation, visual and linguistic semantic reconstruction, and real-time closed-loop interaction. This paper takes the evolution of EEG intelligent decoding technology as the main thread, reviews representative advances in control intent decoding, brain state decoding, high-level semantic decoding, general representation learning, and closed-loop interaction, and further analyzes the application evidence of these technologies in stroke rehabilitation, language and visual function rehabilitation, epilepsy detection and early warning, consciousness disorder assessment, and assisted diagnosis of mental disorders. Existing research shows that the levels of information represented and the scope of application of EEG decoding are continuously expanding, but there are significant differences in clinical maturity across different directions: control intention recognition, abnormality detection, and closed nerve loop rehabilitation have accumulated some research foundations, while visual and linguistic semantic decoding remains mainly in healthy subjects and controlled experimental conditions. Currently, the key constraints on clinical translation are not only model performance but also include cross-individual and cross-center generalization, neural information fidelity, long-term stability, incremental clinical value, and closed-loop safety and ethical regulation. In the future, guided by real clinical needs and patient outcomes, intelligent EEG decoding should be promoted from method feasibility verification to stable, reliable, and evaluable clinical applications.