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This paper discovers a shared valence axis (V-axis) across modern LLMs and human EEG signals, showing that a single direction from LLM internal representations aligns with neural responses to emotional stimuli. It also identifies the saturation regularity, explaining why LLM-derived supervision fails to improve EEG decoding and how leveraging residual diversity boosts performance.
Brain-IT-VQA framework decodes visual content from fMRI signals using transformer architecture, outperforming previous methods. The authors also introduce NSD-VQA, a new dataset with richer annotations for evaluating fMRI-based visual question answering.
This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.