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The human brain possesses remarkable abilities in visual processing,including image recognition and scene summarization. Efforts have been made tounderstand the cognitive capacities of the visual brain, but a comprehensiveunderstanding of the underlying mechanisms still needs to be discovered.Advancements in brain decoding techniques have led to sophisticated approacheslike fMRI-to-Image reconstruction, which has implications for cognitiveneuroscience and medical imaging. However, challenges persist in fMRI-to-imagereconstruction, such as incorporating global context and contextualinformation. In this article, we propose fMRI captioning, where captions aregenerated based on fMRI data to gain insight into the neural correlates ofvisual perception. This research presents DreamCatcher, a novel framework forfMRI captioning. DreamCatcher consists of the Representation Space Encoder(RSE) and the RevEmbedding Decoder, which transform fMRI vectors into a latentspace and generate captions, respectively. We evaluated the framework throughvisualization, dataset training, and testing on subjects, demonstrating strongperformance. fMRI-based captioning has diverse applications, includingunderstanding neural mechanisms, Human-Computer Interaction, and enhancinglearning and training processes.

DreamCatcher: Revealing the Language of the Brain with fMRI using GPT Embedding