Reconstructing music perception from brain activity using a prior guided diffusion model.

ABSTRACT: Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-subject functional alignment with bayesian decoding in the latent space of a diffusion-based audio generator. Functional alignment projects individual fMRI responses onto a shared representational manifold, increasing the performance of cross-participant accuracy with respect to anatomically normalized baselines. A bayesian search over latent trajectories then selects the most plausible waveform candidate, stabilizing reconstructions against neural noise. Crucially, we bridge CLAP's multi-modal embeddings to music-domain latents through a dedicat

SUBMITTER: Ciferri M 

PROVIDER: S-EPMC12658000 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature