Naturalistic Music Decoding from EEG Data via Latent Diffusion Models

2024-05-15

arXiv.org

AI Summary

This study addresses the challenge of end-to-end reconstruction of high-fidelity, polyphonic, harmonically rich natural music directly from raw, non-invasive EEG signals. Methodologically, it introduces latent diffusion models (LDMs) to the EEG-to-audio decoding task for the first time, enabling direct mapping from raw temporal EEG to complex audio waveforms—without handcrafted features, channel selection, or signal preprocessing. A novel neural-embedding-based evaluation metric is proposed to better quantify auditory perceptual consistency. Experiments on the NMED-T dataset demonstrate substantial improvements in timbral and structural fidelity; reconstructed audio exhibits superior intelligibility and musicality compared to conventional linear models and VAE baselines. Key contributions include: (1) the first LDM framework explicitly designed for natural-music synthesis from EEG; (2) a full-brain, end-to-end decoding paradigm that maps raw EEG to high-quality audio; and (3) a semantics-aware evaluation framework tailored for neural decoding.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Brain Signals
• Natural Music Reconstruction
• High-quality Complex Signals

Innovation

• Latent Diffusion Models
• EEG-based Music Reconstruction
• Brain Signal Interpretation