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

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  • معلومة اضافية
    • بيانات النشر:
      Zenodo
    • الموضوع:
      2021
    • Collection:
      Zenodo
    • نبذة مختصرة :
      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).
    • Relation:
      https://doi.org/10.5281/zenodo.4917217; https://doi.org/10.5281/zenodo.4917218; oai:zenodo.org:4917218
    • الرقم المعرف:
      10.5281/zenodo.4917218
    • الدخول الالكتروني :
      https://doi.org/10.5281/zenodo.4917218
    • Rights:
      info:eu-repo/semantics/openAccess ; Creative Commons Attribution Share Alike 4.0 International ; https://creativecommons.org/licenses/by-sa/4.0/legalcode
    • الرقم المعرف:
      edsbas.170709DA