Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
Abstract
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbres, such as MIDI-generated tunes or monophonic pieces, the focus here is on intricate music featuring a diverse array of instruments, voices, and effects, rich in harmonics and timbre. This study represents an initial foray into achieving general music reconstruction of high-quality using non-invasive EEG data, employing an end-to-end training approach directly on raw data without the need for manual pre-processing and channel selection. We train our models on the public NMED-T dataset and perform quantitative evaluation proposing neural embedding-based metrics. Our work contributes to the ongoing research in neural decoding and brain-computer interfaces, offering insights into the feasibility of using EEG data for complex auditory information reconstruction.
Cite
@article{arxiv.2405.09062,
title = {Naturalistic Music Decoding from EEG Data via Latent Diffusion Models},
author = {Emilian Postolache and Natalia Polouliakh and Hiroaki Kitano and Akima Connelly and Emanuele Rodolà and Luca Cosmo and Taketo Akama},
journal= {arXiv preprint arXiv:2405.09062},
year = {2025}
}
Comments
Accepted at ICASSP-25