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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-8666334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT nakaitomoya musicgenreneuroimagingdataset AT koidemajimanaoko musicgenreneuroimagingdataset AT nishimotoshinji musicgenreneuroimagingdataset |