  Decoding non-invasive brain recordings is pivotal for advancing our
understanding of human cognition but faces challenges due to individual
differences and complex neural signal representations. Traditional methods
often require customized models and extensive trials, lacking interpretability
in visual reconstruction tasks. Our framework integrates 3D brain structures
with visual semantics using a Vision Transformer 3D. This unified feature
extractor efficiently aligns fMRI features with multiple levels of visual
embeddings, eliminating the need for subject-specific models and allowing
extraction from single-trial data. The extractor consolidates multi-level
visual features into one network, simplifying integration with Large Language
Models (LLMs). Additionally, we have enhanced the fMRI dataset with diverse
fMRI-image-related textual data to support multimodal large model development.
Integrating with LLMs enhances decoding capabilities, enabling tasks such as
brain captioning, complex reasoning, concept localization, and visual
reconstruction. Our approach demonstrates superior performance across these
tasks, precisely identifying language-based concepts within brain signals,
enhancing interpretability, and providing deeper insights into neural
processes. These advances significantly broaden the applicability of
non-invasive brain decoding in neuroscience and human-computer interaction,
setting the stage for advanced brain-computer interfaces and cognitive models.
