Brain-computer interface （BCI） establishes information pathways between the human brain and external devices by analyzing brain activity， providing new technical means for assisted communication， functional assessment， rehabilitation training， and neuromodulation. Electroencephalography （EEG） is one of the most widely used signal modalities in non-invasive BCI and brain health research because of its non-invasiveness， low cost， high temporal resolution， and ease of repeated acquisition. Driven by recent advances in artificial intelligence， EEG decoding has expanded from conventional control command recognition to complex brain-state representation， visual and linguistic semantic reconstruction， and real-time closed-loop interaction； accordingly， this paper reviews representative progress in control intent decoding， brain-state decoding， high-level semantic decoding， general representation learning， and closed-loop interaction， and analyzes their applications in stroke rehabilitation， language and visual function rehabilitation， epilepsy detection and early warning， assessment of disorders of consciousness， and auxiliary diagnosis of mental disorders. Although the representational capacity and application scope of EEG decoding continue to expand， clinical maturity varies markedly across different directions： control intent recognition， abnormal-state detection， and closed-loop neurorehabilitation have accumulated a certain research foundation， whereas visual and linguistic semantic decoding remains largely limited to healthy participants and controlled experimental settings； major barriers to clinical translation include cross-individual and cross-center generalization， neural information fidelity， long-term stability， incremental clinical value， closed-loop safety， and ethical regulation. Future research should be guided by real clinical needs and patient outcomes to advance intelligent EEG decoding from methodological feasibility toward stable， reliable， and evaluable clinical applications.