{
  "Name": "Learning from label proportions for a visual matrix speller (ERP)",
  "BIDSVersion": "1.9.0",
  "HEDVersion": "8.4.0",
  "DatasetType": "derivative",
  "License": "CC-BY-4.0",
  "Authors": [
    "David Hübner",
    "Thibault Verhoeven",
    "Konstantin Schmid",
    "Klaus-Robert Müller",
    "Michael Tangermann",
    "Pieter-Jan Kindermans"
  ],
  "Funding": [
    "BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG), grant number EXC 1086",
    "bwHPC initiative, grant INST 39/963-1 FUGG",
    "European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 657679",
    "Special Research Fund from Ghent University",
    "BK21 program funded by Korean National Research Foundation grant No. 2012-005741"
  ],
  "EthicsApprovals": [
    "Ethics Committee of the University Medical Center Freiburg",
    "Declaration of Helsinki"
  ],
  "ReferencesAndLinks": [
    "http://doi.org/10.5281/zenodo.192684"
  ],
  "GeneratedBy": [
    {
      "CodeURL": "https://github.com/NeuroTechX/moabb",
      "Name": "moabb",
      "Description": "Mother of All BCI Benchmarks",
      "Version": "1.5.0"
    }
  ],
  "SourceDatasets": [
    {
      "DOI": "10.1371/journal.pone.0175856",
      "URL": "http://doi.org/10.5281/zenodo.192684"
    }
  ],
  "Keywords": [
    "brain-computer interface",
    "BCI",
    "event-related potentials",
    "ERP",
    "P300",
    "learning from label proportions",
    "LLP",
    "unsupervised learning",
    "calibrationless",
    "visual speller"
  ],
  "PublicationYear": 2017,
  "DatasetDOI": "10.82901/nemar.nm000199",
  "Version": "1.0.2"
}