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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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  <dc:creator>Hinss, Marcel F.</dc:creator>
  <dc:creator>Darmet, Ludovic</dc:creator>
  <dc:creator>Somon, Bertille</dc:creator>
  <dc:creator>Jahanpour, Emilie</dc:creator>
  <dc:creator>Lotte, Fabien</dc:creator>
  <dc:creator>Ladouce, Simon</dc:creator>
  <dc:creator>Roy, Raphaëlle N.</dc:creator>
  <dc:date>2021-07-01</dc:date>
  <dc:description>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).

 

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.</dc:description>
  <dc:description>The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342).</dc:description>
  <dc:identifier>https://zenodo.org/record/5055046</dc:identifier>
  <dc:identifier>10.5281/zenodo.5055046</dc:identifier>
  <dc:identifier>oai:zenodo.org:5055046</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.4917217</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-sa/4.0/legalcode</dc:rights>
  <dc:subject>EEG</dc:subject>
  <dc:subject>Workload</dc:subject>
  <dc:subject>MATB</dc:subject>
  <dc:subject>Passive BCI</dc:subject>
  <dc:subject>Classification</dc:subject>
  <dc:subject>Physiological computing</dc:subject>
  <dc:title>An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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