BREAD_brainboost_eeg_artifact_detection/datacite.yml

authors:
  -
    firstname: "Lu"
    lastname: "Wang-Nöth"
    affiliation: "brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany; Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany"
    id: 'ORCID:0009-0002-7443-121X'
  -
    firstname: "Philipp"
    lastname: "Heiler"
    affiliation: "brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany"
  -
    firstname: "Hai"
    lastname: "Huang"
    affiliation: "Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany"
    id: 'ORCID:0000-0001-8745-8142'
  -
    firstname: "Daniel"
    lastname: "Lichtenstern"
    affiliation: "brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany"
  -
    firstname: "Alexandra"
    lastname: "Reichenbach"
    affiliation: "Center for Machine Learning, Heilbronn University, Max-Planck-Str. 39, 74081 Heilbronn, Germany"
    id: 'ORCID:0000-0003-4199-3005'
  -
    firstname: "Luis"
    lastname: "Flacke"
    affiliation: "brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany"
  -
    firstname: "Linus"
    lastname: "Maisch"
    affiliation: "brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany"
  -
    firstname: "Helmut"
    lastname: "Mayer"
    affiliation: "Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany"
    id: 'ORCID:0000-0002-9439-2695'    
    
title: "How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings"

description:
    This EEG dataset, used for EMG artifact detection in EEG recordings, contains both artifact-contaminated signals and resting-state eyes-open (EO) signals.  It includes 932 npy files of EEG recordings from seven subjects, consisting of 664 artifact-containing epochs and 268 EO epochs.

keywords:
  - Neuroscience
  - EEG
  - EMG
  - Artifact Detection
  - Data Cleaning
  - Data Collection Optimization

license:
  name: 'Creative Commons Attribution 4.0'
  url: 'https://creativecommons.org/licenses/by/4.0/deed.en'

funding:
  - 'Federal Ministry for Economic Affairs and Climate Action of Germany, ZIM KK5211501BM0'

references:
  -
    # id: 'doi:tba'
    reftype: "IsSupplementTo"
    citation: "Lu Wang-Nöth, Philipp Heiler, Hai Huang, Daniel Lichtenstern, Alexandra Reichenbach, Luis Flacke, Linus Maisch, Helmut Mayer: How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings. Preprint."

resourcetype: Dataset
templateversion: 1.2