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MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music

Giorgia Cantisani; Gabriel Trégoat; Slim Essid; Gaël Richard


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  <identifier identifierType="DOI">10.5281/zenodo.4537751</identifier>
  <creators>
    <creator>
      <creatorName>Giorgia Cantisani</creatorName>
      <affiliation>LTCI, Télécom Paris, Institut Polytechnique de Paris</affiliation>
    </creator>
    <creator>
      <creatorName>Gabriel Trégoat</creatorName>
    </creator>
    <creator>
      <creatorName>Slim Essid</creatorName>
      <affiliation>LTCI, Télécom Paris, Institut Polytechnique de Paris</affiliation>
    </creator>
    <creator>
      <creatorName>Gaël Richard</creatorName>
      <affiliation>LTCI, Télécom Paris, Institut Polytechnique de Paris</affiliation>
    </creator>
  </creators>
  <titles>
    <title>MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Auditory attention decoding</subject>
    <subject>EEG</subject>
    <subject>Polyphonic music</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-09-19</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4537751</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="Documents" resourceTypeGeneral="ConferencePaper">10.21437/SMM.2019-11</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4537750</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ieee</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/mir</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The&amp;nbsp;&lt;em&gt;&lt;strong&gt;MAD-EEG&amp;nbsp;Dataset&lt;/strong&gt;&lt;/em&gt; is&amp;nbsp;a&amp;nbsp;research&amp;nbsp;corpus&amp;nbsp;for studying&amp;nbsp;EEG-based auditory attention decoding to a target instrument in polyphonic music.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The dataset&amp;nbsp;consists&amp;nbsp;of&amp;nbsp;20-channel&amp;nbsp;EEG&amp;nbsp;responses to music recorded from 8 subjects while attending to a particular instrument in&amp;nbsp;a music mixture.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For further details, please refer to the paper:&amp;nbsp;&lt;em&gt;&lt;a href="https://hal.archives-ouvertes.fr/hal-02291882/document"&gt;MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;If you use the data in your research, please reference the paper (not just&amp;nbsp;the Zenodo record):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@inproceedings{Cantisani2019,
  author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard},
  title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}},
  year=2019,
  booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019},
  pages={51--55},
  doi={10.21437/SMM.2019-11},
  url={http://dx.doi.org/10.21437/SMM.2019-11}
}&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/765068/">765068</awardNumber>
      <awardTitle>New Frontiers in Music Information Processing</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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