Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY SA
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY SA
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY SA
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY SA
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY SA
Data sources: ZENODO
versions View all 3 versions
addClaim

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

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

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

Abstract

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.

The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342).

Keywords

Physiological computing, Passive BCI, EEG, Workload, Classification, MATB

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 296
    download downloads 869
  • 296
    views
    869
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
6
Top 10%
Average
Top 10%
296
869
Related to Research communities