Decoding Naturalistic Music from Electroencephalogram (EEG) Data using Latent Diffusion Models

This study explores the use of latent diffusion models, a powerful class of generative models, to reconstruct complex, naturalistic music from electroencephalogram (EEG) recordings. The proposed method aims to achieve high-quality music reconstruction without the need for manual pre-processing or channel selection of the raw EEG data.