EEG-Guided Music Generation through Cross-Modal Latent Alignment: A Conceptual Framework

This project presents a framework for reconstructing and generating music from electroencephalography (EEG) signals by mapping neural activity into latent audio representation spaces learned by modern generative models. It integrates cross-modal contrastive learning to align EEG features with audio embeddings and uses the inferred latent codes to condition music synthesis systems. The approach aims to enable a brain-signal-to-music pipeline, offering potential applications in neuroscience, brain–computer interfaces, and creative AI.