{
  "Name": "Chen2017 – Single-flicker online SSVEP BCI dataset",
  "BIDSVersion": "1.9.0",
  "HEDVersion": "8.4.0",
  "DatasetType": "derivative",
  "License": "CC BY 4.0",
  "Authors": [
    "Jingjing Chen",
    "Dan Zhang",
    "Andreas K. Engel",
    "Qin Gong",
    "Alexander Maye"
  ],
  "Funding": [
    "DFG TRR169/B1/Z2 Crossmodal Learning",
    "Landesforschungsfoerderung Hamburg CROSS FV25"
  ],
  "EthicsApprovals": [
    "Ethics committee of the medical association, Hamburg"
  ],
  "ReferencesAndLinks": [
    "https://zenodo.org/records/580485"
  ],
  "GeneratedBy": [
    {
      "CodeURL": "https://github.com/NeuroTechX/moabb",
      "Name": "moabb",
      "Description": "Mother of All BCI Benchmarks",
      "Version": "1.4.3"
    }
  ],
  "SourceDatasets": [
    {
      "DOI": "10.1371/journal.pone.0178385",
      "URL": "https://zenodo.org/records/580485"
    }
  ],
  "Keywords": [
    "SSVEP",
    "BCI",
    "spatial navigation",
    "single-flicker",
    "online BCI"
  ],
  "PublicationYear": 2017,
  "ExperimentName": "Chen2017",
  "Description": "Single-flicker online SSVEP BCI dataset.\n\n\nDataset summary:\n\n#Subj          12\n#Chan          32\n#Classes           4\n#Trials / class      varies\nTrials length       3.5 s\nFreq 512/2048 Hz\n#Sessions           2\n\n\nParticipants:\n\n- Population: healthy\n- Age: 23.5 (range: 19-32) years\n\n\nEquipment:\n\n- Amplifier: BioSemi ActiveTwo\n- Electrodes: active\n- Montage: biosemi32\n- Reference: CMS/DRL\n\n\nData Access:\n\n- DOI: 10.1371/journal.pone.0178385\n- Data URL: https://zenodo.org/records/580485\n- Repository: Zenodo\n\n\nExperimental Protocol:\n\n- Paradigm: ssvep\n- Task type: spatial navigation\n- Feedback: visual\n- Stimulus: single-flicker spatially coded\n\nDataset from [1]_.\n\nThis dataset uses a spatially coded SSVEP paradigm where a single white\nsquare flickers at 15 Hz in the center of the screen. Four non-flickering\ntarget squares are placed at the cardinal directions (N, E, W, S). The\nuser gazes at one target, producing a distinct spatial topography of the\n15 Hz SSVEP response for each direction.\n\nThe dataset contains 32-channel EEG recorded from 12 healthy subjects\n(7 female, 5 male, mean age 23.5, range 19-32) using a BioSemi ActiveTwo\nsystem.\n\nTwo sessions are available per subject:\n\n- Session \"0\" (training): Structured calibration data from ``.xdf``\nfiles recorded at 2048 Hz. Each subject has 2 runs of 100 trials\n(50 per direction, 200 total), with ~3.5 s per trial. Requires\n``pyxdf`` (install with ``pip install moabb[xdf]``).\n- Session \"1\" (online): Adaptive BCI game data from ``.mat`` files\nrecorded at 512 Hz. Variable-length trials from approximately 16 game\nrounds per subject.\n\nBoth sessions use the same BioSemi ActiveTwo cap with 32 EEG channels\n(A1-A32) and biosemi32 montage. The sampling rates differ between\nsessions (2048 Hz for training, 512 Hz for online).\n\nWarnings\n--------\nThis paradigm uses a SINGLE flicker frequency (15 Hz) with spatially-coded\ndirections. Standard frequency-based SSVEP analysis (CCA, FBCCA) will NOT\nwork. Use broadband spatial features or classification approaches instead.\n\nReferences\n----------\n.. [1] J. Chen, D. Zhang, A. K. Engel, Q. Gong, and A. Maye,\n\"Application of a single-flicker online SSVEP BCI for spatial\nnavigation,\" PLoS ONE, vol. 12, no. 5, e0178385, 2017.\nDOI: 10.1371/journal.pone.0178385",
  "DatasetDOI": "10.82901/nemar.nm000122",
  "Version": "1.0.2"
}