{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cb3","dataset_id":"nm000223","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":15,"ages":[22,22,22,22,22,22,22,22,22,22,22,22,22,22,22],"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/nm000223","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"8b18109b30b4dc3c2b8990b7be65f593091f487c13a40f02edfa42bc53f0c334","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Electric light control experiment (15 subjects, 4 classes, 31 EEG ch)","readme":"# Electric light control experiment (15 subjects, 4 classes, 31 EEG ch)\nElectric light control experiment (15 subjects, 4 classes, 31 EEG ch).\n## Dataset Overview\n- **Code**: Lee2024-EL\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=22.13, std=2.2\n- **Gender distribution**: male=10, female=5\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 EL 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":663133038,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000223","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:10.404838+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-25T15:29:20Z"},"total_files":465,"computed_title":"Electric light control experiment (15 subjects, 4 classes, 31 EEG ch)","nchans_counts":[{"val":31,"count":465}],"sfreq_counts":[{"val":500.0,"count":465}],"stats_computed_at":"2026-05-01T13:49:34.645870+00:00","total_duration_s":14025.07,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"ebf60080168a5255","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 example by paradigm is the Cross-modal Oddball Task (Parkinson’s) example: it is an oddball/target-vs-standard paradigm used to evoke ERP components (including P300-like responses). That example supports mapping oddball-style target detection to an attention/target-detection construct; however, in the few-shot it is labeled Type=Clinical/Intervention mainly because the recruited population is Parkinson’s disease (clinical focus). For the present dataset, the same oddball/P300 convention applies, but without a clinical cohort, so Type should reflect the cognitive construct (target detection/attention) rather than clinical emphasis.","metadata_analysis":"Key facts from metadata/readme: (1) Population: \"**Health status**: healthy\" and \"**Number of subjects**: 15\". (2) Paradigm: \"**Paradigm**: p300\" and \"**Study design**: P300 BCI for EL home appliance control; 4-class oddball; LCD display\". (3) Stimulus modality: \"**Stimulus type**: flash\" and \"**Stimulus modalities**: visual\" / \"**Primary modality**: visual\". (4) Event structure consistent with oddball: \"**Events**: Target=2, NonTarget=1\" and \"Class labels: Target, NonTarget\".","paper_abstract_analysis":"No useful paper information (abstract not provided in the input metadata).","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\" (ALIGNS with few-shot convention that non-clinical participants are labeled Healthy). Modality: Metadata says \"Stimulus modalities: visual\" and \"Stimulus type: flash\" (ALIGNS with few-shot convention that modality is the stimulus channel, i.e., Visual). Type: Metadata says \"Paradigm: p300\" and indicates an oddball target/non-target design for BCI control (few-shot pattern from oddball example suggests an attention/target-detection construct; there is no conflicting explicit metadata demanding Clinical/Intervention).","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Healthy (WINNER) — evidence: \"Health status: healthy\"; also no mention of any diagnosis.\n2) Unknown (RUNNER-UP) — only if health status were unclear, but it is explicit.\nAlignment: aligns.\n\nModality:\n1) Visual (WINNER) — evidence: \"Stimulus modalities: visual\", \"Primary modality: visual\", \"Stimulus type: flash\".\n2) Other (RUNNER-UP) — only if non-standard sensory input existed, but not supported.\nAlignment: aligns.\n\nType:\n1) Attention (WINNER) — evidence: P300/oddball target detection: \"Paradigm: p300\", \"Events: Target=2, NonTarget=1\", and \"4-class oddball\" implies attention to infrequent targets.\n2) Perception (RUNNER-UP) — could apply because stimuli are visual flashes, but the core construct in a P300 oddball/BCI is target detection/attentional selection rather than sensory discrimination.\nAlignment: aligns with oddball few-shot convention; no metadata conflict.\n\nConfidence justification:\n- Pathology 0.9: explicit quote \"Health status: healthy\" plus consistent non-clinical description.\n- Modality 0.9: multiple explicit quotes (\"Stimulus modalities: visual\", \"Primary modality: visual\", \"Stimulus type: flash\").\n- Type 0.8: explicit oddball/P300 target vs non-target quotes (\"Paradigm: p300\", \"Events: Target=2, NonTarget=1\", \"4-class oddball\") with a strong few-shot analog (oddball example) supporting attention/target-detection framing."}},"canonical_name":null,"name_confidence":0.62,"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":"Lee2024_Electric_light_control"}}