A Review of Multimodal Brain Language Decoding

Brain language decoding is a frontier field bridging cognitive neuroscience and artificial intelligence, which aims to reconstruct language cognition from neural signals. This review summarizes recent progress in multi-modal decoding approaches. Methodologically, it highlights advancements in integrating neural acquisition techniques such as fMRI, EEG, and ECoG with deep neural network modeling. Theoretically, it explores core issues including signal spatiotemporal characteristics, feature alignment, and cross-modal generalization. At the application level, the paper assess the translational potential of this technology in clinical language disorder interventions and intelligent brain-computer interfaces. To address challenges such as individual variability and limited high-quality data, it discusses strategies including universal decoding frameworks and adaptive learning algorithms. This study also reflects on ethical concerns related to neurotechnology. Together, this work outlines a systematic framework and technical roadmap for advancing brain-language decoding in both fundamental research and real-world applications.