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An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021

Hinss, Marcel F.; Darmet, Ludovic; Somon, Bertille; Jahanpour, Emilie; Lotte, Fabien; Ladouce, Simon; Roy, Raphaëlle N.


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  <identifier identifierType="DOI">10.5281/zenodo.5055046</identifier>
  <creators>
    <creator>
      <creatorName>Hinss, Marcel F.</creatorName>
      <givenName>Marcel F.</givenName>
      <familyName>Hinss</familyName>
      <affiliation>Univ. Maastricht, NL</affiliation>
    </creator>
    <creator>
      <creatorName>Darmet, Ludovic</creatorName>
      <givenName>Ludovic</givenName>
      <familyName>Darmet</familyName>
      <affiliation>ISAE-SUPAERO, Univ. Toulouse, France</affiliation>
    </creator>
    <creator>
      <creatorName>Somon, Bertille</creatorName>
      <givenName>Bertille</givenName>
      <familyName>Somon</familyName>
      <affiliation>ISAE-SUPAERO, Univ. Toulouse, France</affiliation>
    </creator>
    <creator>
      <creatorName>Jahanpour, Emilie</creatorName>
      <givenName>Emilie</givenName>
      <familyName>Jahanpour</familyName>
      <affiliation>ISAE-SUPAERO, Univ. Toulouse, France</affiliation>
    </creator>
    <creator>
      <creatorName>Lotte, Fabien</creatorName>
      <givenName>Fabien</givenName>
      <familyName>Lotte</familyName>
      <affiliation>Inria Bordeaux Sud-Ouest, Talence, France</affiliation>
    </creator>
    <creator>
      <creatorName>Ladouce, Simon</creatorName>
      <givenName>Simon</givenName>
      <familyName>Ladouce</familyName>
      <affiliation>ISAE-SUPAERO, Univ. Toulouse, France</affiliation>
    </creator>
    <creator>
      <creatorName>Roy, Raphaëlle N.</creatorName>
      <givenName>Raphaëlle N.</givenName>
      <familyName>Roy</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4258-8397</nameIdentifier>
      <affiliation>ISAE-SUPAERO, Univ. Toulouse, France</affiliation>
    </creator>
  </creators>
  <titles>
    <title>An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>EEG</subject>
    <subject>Workload</subject>
    <subject>MATB</subject>
    <subject>Passive BCI</subject>
    <subject>Classification</subject>
    <subject>Physiological computing</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-07-01</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5055046</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4917217</relatedIdentifier>
  </relatedIdentifiers>
  <version>2</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 dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.&lt;/strong&gt;&lt;/p&gt;</description>
    <description descriptionType="Other">The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342).</description>
  </descriptions>
</resource>
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