ArticleData in brief2022
Music genre neuroimaging dataset.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Text-to-music generation models capture musical semantic representations in the human brain.Nature communications · 2025Article
- Reconstructing music perception from brain activity using a prior guided diffusion model.Scientific reports · 2025Article
- Exploring an EM-algorithm for banded regression in computational neuroscience.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the application of machine learning algorithms. Test stimuli were repeated four times and can be used to evaluate the signal to noise ratio of brain activity. Using this dataset, both neuroimaging and machine learning researchers can test multiple algorithms regarding the prediction performance of brain activity induced by various music stimuli. Although two previous studies have used this dataset, there remains much room to apply different acoustic models. This dataset can contribute to integration of the fields of auditory neuroscience and machine learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.