AI RESEARCH
Seeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data
arXiv CS.AI
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ArXi:2604.15374v1 Announce Type: cross Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art (SOTA) perception decoder (DynaDiff) can be adapted to reconstruct imagined content from the Imagery-NSD benchmark.