{
  "id": "beta-4ch",
  "title": "SSVEP · 4 channels",
  "short": "Transfer to a new person",
  "dataset": "BETA",
  "subtitle": "40 visual targets · 4 posterior electrodes",
  "type": "accuracy",
  "subjects": 70,
  "observations": "11,200 trials · 7 participant-disjoint folds",
  "exposure": "4 channels selected from a 64-channel recording · 2-second windows",
  "status": "Research preview",
  "xLabel": "Macro F1",
  "yLabel": "Balanced accuracy",
  "limitation": "Near-floor scores are not ordered reliably between the 8- and 4-electrode subsets. Electrode subsets from laboratory recordings do not validate a physical low-channel cap. Prompted SSVEP does not measure idle false activations. Single-seed results; pretraining overlap unknown.",
  "protocol": [
    "Seven participant-disjoint folds: train on 60 people, test on ten. All four blocks stay with their participant.",
    "Two seconds from stimulus onset; no visual-latency shift. Source data were already zero-phase filtered. Additional 6–80 Hz filtering applies to each selected window separately.",
    "Microvolt units are inferred from an independently documented loader, not explicitly stated in the author MAT description. Inconsistent phase metadata are unused by all methods.",
    "Standard CCA uses known frequencies and three harmonics without training labels. Frozen encoders use training-only standardized ridge heads (alpha 100). EEGNet trains from scratch for 20 epochs with one seed (20260912).",
    "Electrodes: POZ, O1, OZ, O2.",
    "Uniform-guessing reference: 2.5%. Descriptive 95% intervals resample participants; training sets overlap across folds.",
    "No cross-task overall ranking, model fine-tuning optimum or hardware benchmark is claimed."
  ],
  "protocolId": "beta-ssvep-2s-posterior4and8-subject7fold-v1/posterior4",
  "version": "Hugging Face mirror of BETA database, Figshare record 12264401 v3 · {\"mirror\": \"d4290c0200db8a104e0f557349dc49f90ba79506\", \"upstream\": \"Figshare version 3, 2022-06-15\"}",
  "elapsed": 153.87726529221982,
  "peakGb": null,
  "auditSha": "ccddf911864b8471f488e46cae45a3651369dae1134afcdeaede68bdfcc31950",
  "summarySha": "547a35763f7bca10a65c7b44c2f8c46af290159c692d237e92cd682fc21127a6",
  "protocolSha": "dcd1d2bf4dbb46b7db8c4562623448633cde4641fa372c90f25ba9d84012c2f0",
  "source": "https://figshare.com/articles/dataset/The_BETA_database/12264401",
  "backend": "Apple M5 / MPS and CPU",
  "chanceLevel": 2.5,
  "selection": "One fixed seed and training budget; multi-seed sensitivity pending.",
  "license": "CC BY 4.0",
  "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
  "attribution": "Bingchuan Liu et al. · BETA: A Large Benchmark Database Toward SSVEP-BCI Application (2020), doi:10.3389/fnins.2020.00627. Figshare 12264401 v3; mirror Bingchuan/BETA.",
  "privacyReview": "Paper reports Tsinghua University ethics approval 20190002 and written consent; parents consented for participants under 16. Ages span 9-64. Only cohort aggregates should be shown.",
  "reviewedAt": "2026-09-20",
  "reviewBasis": [
    "https://figshare.com/articles/dataset/The_BETA_database/12264401",
    "https://doi.org/10.3389/fnins.2020.00627"
  ],
  "rightsScope": "Personal noncommercial research; aggregate results only",
  "pretrainingOverlap": "Unknown unless explicitly documented; no unseen-pretraining claim."
}