title: Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
summary: 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.
paper_authors._id: 66460e85dfab02c1ba3f439d
paper_authors.name: Emilian Postolache
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-20T07:38:47.672Z
paper_authors._id: 630a39aafaaea618ebc28bf3
paper_authors.avatarUrl: /avatars/6b3e116a2901c842dfc19935fdf7ba7a.svg
paper_authors.fullname: Postolache
paper_authors.type: user
paper_authors.user: Emilian
paper_authors._id: 66460e85dfab02c1ba3f439e
paper_authors.name: Natalia Polouliakh
paper_authors._id: 66460e85dfab02c1ba3f439f
paper_authors.name: Hiroaki Kitano
paper_authors._id: 66460e85dfab02c1ba3f43a0
paper_authors.name: Akima Connelly
paper_authors._id: 66460e85dfab02c1ba3f43a1
paper_authors.name: Emanuele Rodolà
paper_authors._id: 66460e85dfab02c1ba3f43a2
paper_authors.name: Taketo Akama
paper_title: Naturalistic Music Decoding from EEG Data via Latent Diffusion Models
paper_summary: 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.
paper_ai_summary: Latent diffusion models are employed to reconstruct naturalistic music from EEG recordings using an end-to-end training approach and evaluating with neural embedding-based metrics.
paper_ai_keywords: latent diffusion models generative models music reconstruction EEG recordings NMED-T dataset neural embedding-based metrics song classification neural decoding brain-computer interfaces