{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4ca5","dataset_id":"nm000208","associated_paper_doi":null,"authors":["Jongmin Lee","Minju Kim","Dojin Heo","Jongsu Kim","Min-Ki Kim","Taejun Lee","Jongwoo Park","HyunYoung Kim","Minho Hwang","Laehyun Kim","Sung-Phil Kim"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":14,"ages":[22,22,22,22,22,22,22,22,22,22,22,22,22,22],"age_min":22,"age_max":22,"age_mean":22.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000208","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"92556dde313c2f0d512316b12cc1ed334fe8f72cd58640b567843368217d65db","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)","readme":"# Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)\nDoor lock control experiment (15 subjects, 4 classes, 31 EEG ch).\n## Dataset Overview\n- **Code**: Lee2024-DL\n- **Paradigm**: p300\n- **DOI**: 10.3389/fnhum.2024.1320457\n- **Subjects**: 15\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1] s\n- **File format**: MATLAB\n## Acquisition\n- **Sampling rate**: 500.0 Hz\n- **Number of channels**: 31\n- **Channel types**: eeg=31\n- **Channel names**: Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, FT9, FC5, FC1, FC2, FC6, FT10, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, O1, Oz, O2\n- **Montage**: standard_1020\n- **Hardware**: actiCHamp (Brain Products)\n- **Reference**: linked mastoids\n- **Sensor type**: active\n- **Line frequency**: 60.0 Hz\n## Participants\n- **Number of subjects**: 15\n- **Health status**: healthy\n- **Age**: mean=22.87, std=2.07\n- **Gender distribution**: male=12, female=3\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 1.0 s\n- **Study design**: P300 BCI for DL home appliance control; 4-class oddball; LCD display\n- **Feedback type**: visual\n- **Stimulus type**: flash\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Mode**: online\n## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: p300\n- **Stimulus onset asynchrony**: 750.0 ms\n## Data Structure\n- **Trials**: 50 training + 30 testing blocks per subject\n- **Trials context**: per_subject\n## BCI Application\n- **Applications**: home_appliance_control\n- **Environment**: laboratory\n- **Online feedback**: True\n## Tags\n- **Pathology**: Healthy\n- **Modality**: ERP\n- **Type**: P300\n## Documentation\n- **DOI**: 10.3389/fnhum.2024.1320457\n- **License**: CC-BY-4.0\n- **Investigators**: Jongmin Lee, Minju Kim, Dojin Heo, Jongsu Kim, Min-Ki Kim, Taejun Lee, Jongwoo Park, HyunYoung Kim, Minho Hwang, Laehyun Kim, Sung-Phil Kim\n- **Institution**: Ulsan National Institute of Science and Technology\n- **Country**: KR\n- **Data URL**: https://github.com/jml226/Home-Appliance-Control-Dataset\n- **Publication year**: 2024\n## References\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0"],"size_bytes":639231650,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000208","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:03.226228+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-24T03:13:27Z"},"total_files":434,"computed_title":"Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)","nchans_counts":[{"val":31,"count":434}],"sfreq_counts":[{"val":500.0,"count":434}],"stats_computed_at":"2026-05-01T13:49:34.645682+00:00","total_duration_s":13217.372,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"3779cb9ecd5cc15b","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot paradigm is the “Cross-modal Oddball Task” example (Parkinson’s; task labeled Oddball) which shows that an oddball/target-vs-nontarget paradigm is typically treated as an attention/target-detection style ERP experiment, with modality determined by stimulus channel(s). Another relevant convention is the TBI DPX task labeled as Attention, reinforcing that target detection / cue-probe control paradigms map to Attention rather than Motor or Resting-state. In this dataset, the purpose is a visual P300-BCI oddball for device control, which matches the oddball/target-detection convention; unlike the Parkinson’s example, there is no clinical recruitment, so pathology should be Healthy rather than Clinical/Intervention-driven labeling.","metadata_analysis":"Key population facts: the README explicitly states “Health status: healthy” and “Participants… Number of subjects: 15”. It also includes “Tags… Pathology: Healthy”.\n\nKey task/stimulus facts: the README states “Paradigm: p300” and “Study design: P300 BCI for DL home appliance control; 4-class oddball; LCD display”. Sensory channel is explicit: “Stimulus modalities: visual” and “Primary modality: visual”, with “Stimulus type: flash” and HED tags including “Visual-presentation” for both Target and NonTarget.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: “Health status: healthy” and “Tags… Pathology: Healthy”.\n- Few-shot pattern suggests: if a diagnosis is named (e.g., Parkinson’s, TBI), use that; otherwise use Healthy.\n- Alignment: ALIGN (both indicate non-clinical healthy recruitment).\n\nModality:\n- Metadata says: “Stimulus modalities: visual”, “Primary modality: visual”, and HED includes “Visual-presentation”.\n- Few-shot pattern suggests: oddball modality follows the stimulus channel (e.g., cross-modal oddball -> Multisensory; auditory oddball -> Auditory).\n- Alignment: ALIGN (clearly Visual stimuli).\n\nType:\n- Metadata says: “Paradigm: p300”, “Events: Target=2, NonTarget=1”, and “4-class oddball” / “P300 BCI”.\n- Few-shot pattern suggests: oddball/target detection paradigms map to Attention (unless the dataset is primarily a clinical cohort study, then Clinical/Intervention may dominate).\n- Alignment: ALIGN overall; while few-shot examples sometimes emphasize Clinical/Intervention when pathology is present (e.g., Parkinson’s), here pathology is healthy and the paradigm is classic target-detection P300, so Attention is the better fit than Clinical/Intervention.","decision_summary":"Top-2 candidates with head-to-head comparisons:\n\nPathology:\n1) Healthy (WINNER): Supported by “Health status: healthy” and “Tags… Pathology: Healthy”, plus demographics without any diagnosis.\n2) Unknown (RUNNER-UP): Only if health status were missing; not needed here.\nSelection: Healthy. Evidence alignment: ALIGN.\n\nModality:\n1) Visual (WINNER): “Stimulus modalities: visual”, “Primary modality: visual”, HED “Visual-presentation”, and “Stimulus type: flash”.\n2) Other (RUNNER-UP): Only if stimuli were not specified or were non-standard.\nSelection: Visual. Evidence alignment: ALIGN.\n\nType:\n1) Attention (WINNER): Target vs non-target oddball with explicit “Paradigm: p300” and “Events: Target… NonTarget…”, consistent with attentional target detection in P300-ERP/BCI.\n2) Perception (RUNNER-UP): Could apply because it is visual stimulus processing, but the defining construct is target detection/oddball attention rather than sensory discrimination.\nSelection: Attention. Evidence alignment: ALIGN (few-shot oddball conventions + metadata P300/Target-NonTarget)."}},"canonical_name":null,"name_confidence":0.66,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Lee2024_Door_lock_control"}}