{"dataset":{"id":"241","dataset_id":"nm000208","name":"Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)","description":"This dataset comprises EEG recordings from 15 healthy subjects performing a P300-based brain-computer interface task for door lock control. The experiment employed a visual oddball paradigm with 31-channel EEG acquisition at 500 Hz, generating event-related potentials in response to target and non-target stimuli presented on an LCD display. The dataset includes 50 training and 30 testing trials per subject and serves as a benchmark for P300-based BCI applications in home appliance control.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000208","concept_doi":"10.82901/nemar.nm000208","latest_version_doi":"10.82901/nemar.nm000208.v1.0.3","created_at":"2026-03-24 03:12:22","updated_at":"2026-08-18 21:12:56","zenodo_concept_id":"20518314","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)\",\n  \"description\": \"This dataset comprises EEG recordings from 15 healthy subjects performing a P300-based brain-computer interface task for door lock control. The experiment employed a visual oddball paradigm with 31-channel EEG acquisition at 500 Hz, generating event-related potentials in response to target and non-target stimuli presented on an LCD display. The dataset includes 50 training and 30 testing trials per subject and serves as a benchmark for P300-based BCI applications in home appliance control.\",\n  \"methods_description\": \"EEG data were acquired using a 31-channel actiCHamp system (Brain Products) at 500 Hz sampling rate with linked mastoid reference and active sensors. Participants performed a visual oddball task with target and non-target stimuli presented via flash on an LCD display with 750 ms stimulus onset asynchrony. Trial duration was 1.0 second with online visual feedback provided to subjects.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Jongmin Lee\": {},\n    \"Minju Kim\": {},\n    \"Dojin Heo\": {},\n    \"Jongsu Kim\": {},\n    \"Min-Ki Kim\": {},\n    \"Taejun Lee\": {},\n    \"Jongwoo Park\": {},\n    \"HyunYoung Kim\": {},\n    \"Minho Hwang\": {},\n    \"Laehyun Kim\": {},\n    \"Sung-Phil Kim\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"P300\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"oddball paradigm\"\n    },\n    {\n      \"term\": \"home appliance control\"\n    },\n    {\n      \"term\": \"healthy\"\n    },\n    {\n      \"term\": \"ERP\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnhum.2024.1320457\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000208\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000208\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Ulsan National Institute of Science and Technology\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.3 GB (915 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"b712d9e08dcdea8c34f7f271ac94752d8ca9acfba817bc1a817f59bc5b0a7791\"\n}","last_activity_at":"2026-08-18 18:10:23","source":null,"source_id":null,"subject_count":14,"modalities":"eeg","age_min":22.87,"age_max":22.87,"file_size":1336211140,"total_files":3165,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":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","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000208-blue)](https://doi.org/10.82901/nemar.nm000208)\n\n# Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)\n\nDoor lock control experiment (15 subjects, 4 classes, 31 EEG ch).\n\n## Dataset Overview\n\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\n## Acquisition\n\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\n## Participants\n\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\n## Experimental Protocol\n\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\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: p300\n- **Stimulus onset asynchrony**: 750.0 ms\n\n## Data Structure\n\n- **Trials**: 50 training + 30 testing blocks per subject\n- **Trials context**: per_subject\n\n## BCI Application\n\n- **Applications**: home_appliance_control\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: ERP\n- **Type**: P300\n\n## Documentation\n\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\n## References\n\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\n\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. 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