Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
Authors: name: Emilian Postolache, name: Natalia Polouliakh, name: Hiroaki Kitano, name: Akima Connelly, name: Emanuele Rodolà, name: Taketo Akama

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. We additionally perform
song classification based on the generated tracks. 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.