Naturalistic Music Decoding from EEG Data via Latent Diffusion Models

• URL: http://arxiv.org/abs/2405.09062v5
• Date: Wed, 11 Sep 2024 11:36:34 GMT
• Title: Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
• Authors: Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano, Akima Connelly, Emanuele Rodolà, Luca Cosmo, Taketo Akama,
• Abstract summary: This study represents an initial foray into achieving general music reconstruction of high-quality using non-invasive EEG data. We train our models on the public NMED-T dataset and perform quantitative evaluation proposing neural embedding-based metrics.
• Score: 14.882764251306094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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