Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
<|reference_start|>Naturalistic Music Decoding from EEG Data via Latent Diffusion Models: 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.<|reference_end|>
@article{postolache2024naturalistic,
    title={Naturalistic Music Decoding from EEG Data via Latent Diffusion Models},
    author={Emilian Postolache, Natalia Polouliakh, Hiroaki Kitano, Akima
  Connelly, Emanuele Rodol`a, Luca Cosmo, Taketo Akama},
    journal={arXiv preprint arXiv:2405.09062},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09062},
    primaryClass={cs.SD cs.LG eess.AS}
}