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Music genre neuroimaging dataset

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 appli...

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Detalles Bibliográficos
Autores principales: Nakai, Tomoya, Koide-Majima, Naoko, Nishimoto, Shinji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666334/
https://www.ncbi.nlm.nih.gov/pubmed/34917714
http://dx.doi.org/10.1016/j.dib.2021.107675
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author Nakai, Tomoya
Koide-Majima, Naoko
Nishimoto, Shinji
author_facet Nakai, Tomoya
Koide-Majima, Naoko
Nishimoto, Shinji
author_sort Nakai, Tomoya
collection PubMed
description 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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spelling pubmed-86663342021-12-15 Music genre neuroimaging dataset Nakai, Tomoya Koide-Majima, Naoko Nishimoto, Shinji Data Brief Data Article 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. Elsevier 2021-12-04 /pmc/articles/PMC8666334/ /pubmed/34917714 http://dx.doi.org/10.1016/j.dib.2021.107675 Text en © 2021 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Nakai, Tomoya
Koide-Majima, Naoko
Nishimoto, Shinji
Music genre neuroimaging dataset
title Music genre neuroimaging dataset
title_full Music genre neuroimaging dataset
title_fullStr Music genre neuroimaging dataset
title_full_unstemmed Music genre neuroimaging dataset
title_short Music genre neuroimaging dataset
title_sort music genre neuroimaging dataset
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666334/
https://www.ncbi.nlm.nih.gov/pubmed/34917714
http://dx.doi.org/10.1016/j.dib.2021.107675
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