SED-GPT: A Non-Invasive Method for Long-Sequence Fine-Grained Semantics and Emotions Decoding

Traditional emotion decoding methods typically rely on short sequences with limited context and coarse-grained emotion categories. To address these limitations, we proposed the Semantic and Emotion Decoding Generative Pre-trained Transformer (SED-GPT), a non-invasive method for long-sequence fine-grained semantics and emotions decoding on extended narrative stimuli. Using a publicly available fMRI dataset from 8 participants, this exploratory study investigates the feasibility of reconstructing complex semantic and emotional states from brain activity. SED-GPT achieves a BERTScore-F1 of 0.650 on semantic decoding and attains a cosine similarity (CS) of 0.504 and a Jensen–Shannon similarity (JSS) of 0.469 for emotion decoding (p < 0.05). Functional connectivity analyses reveal persistent coupling between the language network and the emotion network, which provides neural evidence for the language–emotion interaction mechanism in Chinese. These findings should be interpreted as pilot-level feasibility evidence.