{"dataset":{"id":"200","dataset_id":"nm000168","name":"BNCI 2015-013 Error-Related Potentials dataset","description":"This dataset comprises EEG recordings from 6 healthy subjects performing an error-related potential (ErrP) monitoring task where participants observe an autonomous cursor moving toward targets with controlled error rates (20-40%). The study demonstrates that error-related brain potentials can be detected and decoded during passive monitoring of external agents without direct user control, providing foundational evidence for error monitoring in brain-computer interface applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000168","concept_doi":"10.82901/nemar.nm000168","latest_version_doi":"10.82901/nemar.nm000168.v1.0.2","created_at":"2026-03-23 13:11:59","updated_at":"2026-08-18 18:15:35","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BNCI 2015-013 Error-Related Potentials dataset\",\n  \"description\": \"This dataset comprises EEG recordings from 6 healthy subjects performing an error-related potential (ErrP) monitoring task where participants observe an autonomous cursor moving toward targets with controlled error rates (20-40%). The study demonstrates that error-related brain potentials can be detected and decoded during passive monitoring of external agents without direct user control, providing foundational evidence for error monitoring in brain-computer interface applications.\",\n  \"methods_description\": \"EEG data were acquired using a Biosemi ActiveTwo system with 64 active electrodes sampled at 512 Hz. Subjects performed a visual monitoring task observing a cursor (green square) moving toward targets (blue for left, red for right) with 20% or 40% error probability across 20 sessions of 10 blocks each. Each block contained approximately 50-64 trials with a trial duration of 2 seconds and a post-stimulus interval of 0.6 seconds.\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Ricardo Chavarriaga\": {},\n    \"José del R. Millán\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"error-related potentials\"\n    },\n    {\n      \"term\": \"ErrP\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"error monitoring\"\n    },\n    {\n      \"term\": \"reinforcement learning\"\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  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1109/TNSRE.2010.2053387\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000168\",\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/nm000168\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"European Commission\",\n      \"award_number\": \"BACS FP6-IST-027140\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"5.6 GB (136 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"39ba8b8042468c19683e0fb1e43741e496a39f27ec81f2b03372cf0abdc141ff\"\n}","last_activity_at":"2026-08-11 21:06:20","source":null,"source_id":null,"subject_count":6,"modalities":"eeg","age_min":27.83,"age_max":27.83,"file_size":5592860510,"total_files":1346,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Ricardo Chavarriaga, José del R. Millán","license":"CC-BY-NC-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000168-blue)](https://doi.org/10.82901/nemar.nm000168)\n\n# BNCI 2015-013 Error-Related Potentials dataset\n\nBNCI 2015-013 Error-Related Potentials dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2015-013\n- **Paradigm**: p300\n- **DOI**: 10.1109/TNSRE.2010.2053387\n- **Subjects**: 6\n- **Sessions per subject**: 20\n- **Events**: Target=1, NonTarget=2\n- **Trial interval**: [0, 0.6] s\n- **File format**: matlab\n\n## Acquisition\n\n- **Sampling rate**: 512.0 Hz\n- **Number of channels**: 64\n- **Channel types**: eeg=64\n- **Channel names**: Fp1, AF7, AF3, F1, F3, F5, F7, FT7, FC5, FC3, FC1, C1, C3, C5, T7, TP7, CP5, CP3, CP1, P1, P3, P5, P7, P9, PO7, PO3, O1, Iz, Oz, POz, Pz, CPz, Fpz, Fp2, AF8, AF4, AFz, Fz, F2, F4, F6, F8, FT8, FC6, FC4, FC2, FCz, Cz, C2, C4, C6, T8, TP8, CP6, CP4, CP2, P2, P4, P6, P8, P10, PO8, PO4, O2\n- **Montage**: standard_1020\n- **Hardware**: Biosemi ActiveTwo\n- **Sensor type**: active\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 6\n- **Health status**: patients\n- **Clinical population**: Healthy\n- **Age**: mean=27.83, std=2.23\n- **Gender distribution**: male=5, female=1\n- **Handedness**: not reported\n- **BCI experience**: not reported\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Task type**: monitoring\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 2.0 s\n- **Study design**: Error-related potential (ErrP) monitoring task where subjects observe a cursor moving towards a target. The cursor moves autonomously with 20% or 40% error probability. Subjects monitor performance without control.\n- **Feedback type**: visual\n- **Stimulus type**: cursor_movement\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: True\n- **Instructions**: Subjects seat in front of a computer screen and monitor a moving cursor (green square) and target location (blue for left, red for right). No control over cursor movement, only assess whether it performs properly. Fixate center of screen.\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\n## Data Structure\n\n- **Trials**: ~50 trials per block, ~64 trials per block for error_prob=0.20\n- **Blocks per session**: 10\n- **Block duration**: 180.0 s\n- **Trials context**: per_block\n\n## Preprocessing\n\n- **Data state**: raw\n- **Preprocessing applied**: False\n\n## Signal Processing\n\n- **Classifiers**: Gaussian classifier\n- **Feature extraction**: event-related potentials\n- **Frequency bands**: analyzed=[1.0, 10.0] Hz\n\n## Cross-Validation\n\n- **Method**: train-test split\n- **Evaluation type**: cross_session\n\n## Performance (Original Study)\n\n- **Accuracy**: 75.8%\n- **Correct Recognition Rate**: 63.2\n- **Error Recognition Rate**: 75.8\n\n## BCI Application\n\n- **Applications**: error_detection\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Cognitive\n- **Type**: ErrP\n\n## Documentation\n\n- **Description**: Dataset on EEG error-related potentials (ErrPs) elicited when users monitor the behavior of an external autonomous agent. One of the first studies showing that error correlates can be observed and decoded during monitoring of external agents without user control.\n- **DOI**: 10.1109/TNSRE.2010.2053387\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Ricardo Chavarriaga, José del R. Millán\n- **Senior author**: José del R. Millán\n- **Contact**: ricardo.chavarriaga@epfl.ch; jose.millan@epfl.ch\n- **Institution**: Ecole Polytechnique Fédérale de Lausanne\n- **Department**: Defitech Chair in Brain-Machine Interface, CNBI, Center for Neuroprosthetics\n- **Country**: CH\n- **Repository**: BNCI Horizon\n- **Publication year**: 2010\n- **Funding**: EC under Contract BACS FP6-IST-027140\n- **Keywords**: error-related potentials, ErrP, brain-computer interface, reinforcement learning, monitoring, error detection\n\n## References\n\nChavarriaga, R., & Millán, J. D. R. (2010). Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng., 18(4), 381-388. https://doi.org/10.1109/TNSRE.2010.2053387\n\nNotes\n\n.. versionadded:: 1.2.0\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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