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Related Experiment Video

Updated: Jan 10, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Reconstructing music perception from brain activity using a prior guided diffusion model.

Matteo Ciferri¹, Matteo Ferrante², Nicola Toschi^2,3

¹Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy. matteo.ciferri@students.uniroma2.eu.

Scientific Reports

|November 26, 2025

Summary

This summary is machine-generated.

Scientists can now reconstruct music from brain activity using a novel data-driven pipeline. This brain-computer interface approach decodes neural signals to generate recognizable music, advancing auditory neuroscience.

Keywords:

Auditory perception Brain decoding Cognitive science Machine learning

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Related Experiment Videos

Last Updated: Jan 10, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Area of Science:

Cognitive Neuroscience
Neuroscience
Machine Learning

Background:

Reconstructing music from brain activity offers insights into neural processing of sound.
It is crucial for developing advanced brain-computer interfaces (BCIs).

Purpose of the Study:

To develop a fully data-driven pipeline for reconstructing music directly from brain activity.
To enhance cross-subject accuracy in music reconstruction using functional alignment and Bayesian decoding.
To bridge multi-modal embeddings with music-domain latents for improved audio generation.

Main Methods:

Utilized a pipeline combining cross-subject functional alignment and Bayesian decoding within a diffusion-based audio generator's latent space.
Employed functional alignment to project fMRI responses onto a shared manifold, improving cross-participant accuracy.
Integrated CLAP's multi-modal embeddings with music-domain latents via a dedicated aligner, bypassing the need for manual captions.

Main Results:

Achieved a cross-subject-averaged identification accuracy of [Formula: see text] across ten diverse music genres.
Generated audio recognized by human listeners above chance in 85.7% of trials.
Identified key brain regions involved in music reconstruction, including auditory, inferior-frontal, and premotor cortices.

Conclusions:

The developed framework successfully reconstructs music from brain activity, demonstrating a significant advancement in BCIs.
The approach provides a principled method for connecting generative audio models with cognitive neuroscience.
Findings support hierarchical and sensorimotor theories of music perception by localizing predictive neural signals.