{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4ccf","dataset_id":"nm000272","associated_paper_doi":null,"authors":["Michele Romani","Devis Zanoni","Elisabetta Farella","Luca Turchet"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000272","datatypes":["eeg"],"demographics":{"subjects_count":45,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000272","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"8c9e83d65129dcbad6ca5764067ba30917f4bd6dfc170549f6273508a6694ce6","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000272-blue)](https://doi.org/10.82901/nemar.nm000272)\n# BrainForm: a P300 ERP EEG dataset from a serious game for BCI training and data collection\n## Summary\nThis dataset contains scalp electroencephalography (EEG) recordings collected with\n**BrainForm**, a gamified (serious-game) **P300 event-related potential (ERP)**\nbrain–computer interface (BCI) designed for scalable data collection using consumer\nhardware and a minimal setup. Participants completed repeated runs of a P300 spelling /\nselection task, enabling study of BCI skill acquisition across sessions and of the\nperceptual/performance effects of different visual stimulation textures.\nThe BIDS conversion in this NEMAR record contains EEG data for **22 participants**\n(`sub-*` folders), organised under the `eeg/` modality with a P300 task (`task-p300`).\n## Modality and paradigm\n- **Modality:** EEG (scalp electroencephalography)\n- **Task / paradigm:** P300 ERP oddball / speller within a serious game (`task-p300`)\n- **Focus:** BCI training, human factors, machine learning on ERP data\n## Participants\nThis BIDS dataset includes **22 subjects**. The original study used a within-subject\ndesign with multiple runs and two task complexities. Refer to `participants.tsv` and the\npaper for details.\n## Original dataset / data paper\nPlease cite the original paper when using this dataset:\n> Romani, M., Zanoni, D., Farella, E., & Turchet, L. (2025). *BrainForm: a Serious Game\n> for BCI Training and Data Collection.* arXiv:2510.10169.\n> https://doi.org/10.48550/arXiv.2510.10169\n- **DOI / preprint:** [arXiv:2510.10169](https://doi.org/10.48550/arXiv.2510.10169)\n- **Source / project:** University of Trento and Fondazione Bruno Kessler (FBK)\n- **Related record:** https://zenodo.org/records/17225966\n## Attribution\nAll data were collected by the original authors (Michele Romani and colleagues,\nUniversity of Trento / FBK). Please credit the original creators and cite the paper\nabove. This NEMAR record redistributes the dataset in BIDS format; EEG-BIDS and related\nBIDS tools were used only for standardisation, not as the source of the data.\n## License\nCC-BY-4.0 (see `dataset_description.json`).","recording_modality":["eeg"],"senior_author":"Luca Turchet","sessions":["0cb","0grain","1cb","1grain","2cbExtra","2grainExtra"],"size_bytes":248663467,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000272","raw_key":"dataset_description.json","dep_keys":["README.md"]},"study_design":null,"study_domain":null,"tasks":["ERP","p300"],"timestamps":{"digested_at":"2026-08-26T11:04:30.706502+00:00","dataset_created_at":null,"dataset_modified_at":"2026-08-20 19:22:15"},"total_files":181,"computed_title":"Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)","nchans_counts":[{"val":8,"count":240},{"val":9,"count":61}],"sfreq_counts":[{"val":250.0,"count":301}],"stats_computed_at":"2026-08-27T07:51:42.858515+00:00","total_duration_s":68241.664,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"55d88e7eb05a9edb","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.55,"modality":0.6,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot conventions are the oddball/P300-style paradigms. Example: \"Cross-modal Oddball Task\" is labeled with Type=Clinical/Intervention because it explicitly recruits Parkinson’s disease patients, but it demonstrates the convention that oddball/P300 paradigms map to an attention/cognitive-control style Type label rather than Motor/Resting-state. Another relevant convention is the \"Three-Stim Auditory Oddball\" dataset showing that when the paradigm is explicitly auditory tones, Modality=Auditory; by analogy, if the paradigm is P300 without further details, Modality must be inferred cautiously (often visual P300/speller, but not guaranteed).","metadata_analysis":"Available metadata is sparse. Key snippets:\n- Title: \"romani-bf2025-erp - NEMAR Dataset\"\n- Tasks list includes: \"p300\"\n- Participants: \"Subjects: 22\"\nFrom \"p300\" and \"erp\" in the title, the dataset likely contains an ERP P300 paradigm (commonly an oddball/target-detection task). There is no explicit mention of a clinical recruitment group/diagnosis, and no description of stimulus type (visual flashes/letters vs auditory tones).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"Subjects: 22\" with no diagnosis/group labels.\n- Few-shot pattern suggests: when no clinical population is stated, label often defaults to Healthy, but this is not an explicit fact here.\n- Alignment: CONFLICT/INSUFFICIENT METADATA. Since no recruitment condition is stated, choose Unknown (metadata insufficiency outweighs pattern-based assumption).\n\nModality:\n- Metadata says: task name \"p300\" and dataset title includes \"erp\", but no stimulus channel specified.\n- Few-shot pattern suggests: P300/oddball can be Visual (e.g., visual discrimination/target detection) or Auditory (tone oddball); cross-modal examples show Modality follows stimulus channel.\n- Alignment: PARTIAL. We can only infer weakly; choose Visual as the most common P300 ERP implementation in EEG repositories (often visual P300/speller), but acknowledge ambiguity.\n\nType:\n- Metadata says: \"p300\" / \"erp\" implies a target-detection ERP paradigm.\n- Few-shot pattern suggests: oddball/P300 paradigms are typically categorized under Attention (target detection, oddball processing) rather than Perception or Motor.\n- Alignment: ALIGNS (paradigm-level match), though still inferred due to sparse task description.","decision_summary":"Top-2 candidates and final selections:\n\nPathology:\n1) Unknown — supported by lack of any clinical recruitment info (\"Subjects: 22\" only; no diagnosis/groups given).\n2) Healthy — plausible default if typical ERP study with volunteers, but not explicitly stated.\nWinner: Unknown. Evidence alignment: insufficient metadata to assert Healthy.\nConfidence basis: only negative evidence (absence of diagnosis) → moderate-low.\n\nModality:\n1) Visual — plausible because many EEG \"p300\" tasks are visual P300/speller/visual oddball; supported only indirectly by \"p300\" and \"erp\".\n2) Auditory — equally plausible because P300 is also frequently elicited via auditory oddball.\nWinner: Visual (weak inference). Evidence alignment: ambiguous.\nConfidence basis: no explicit stimulus description; inference only.\n\nType:\n1) Attention — P300 commonly indexes attention/target detection; guided by oddball/P300 few-shot conventions.\n2) Perception — alternative if treated as sensory discrimination, but P300 framing more often emphasizes attentional target processing.\nWinner: Attention. Evidence alignment: paradigm-level match.\nConfidence basis: task name explicitly \"p300\" plus ERP framing in title, but no further details."}},"canonical_name":null,"name_confidence":0.42,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Romani2025_BF_ERP","associated_paper_meta":{"channel":"nemar/IsDerivedFrom","confidence":"high","author_overlap":4,"is_oa":true,"oa_status":"preprint","source":"paper_resolver"},"acknowledgements":"University of Trento and Fondazione Bruno Kessler","references_and_links":["https://doi.org/10.48550/arXiv.2510.10169","https://zenodo.org/records/17225966"],"source_datasets":[{"DOI":"doi:10.48550/arXiv.2510.10169"}],"bad_channels_info":null}}