{"dataset":{"id":"239","dataset_id":"nm000206","name":"Neuroergonomic 2021 dataset","description":"A multi-session EEG dataset acquired from 15 healthy participants performing resting state and graded cognitive tasks (MATB-II at three difficulty levels). The dataset comprises 62-channel EEG recordings at 500 Hz sampling rate designed for passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. Raw EEG data are provided with standardized event annotations using HED 8.4.0 schema and MOABB-compatible feature extraction pipelines (bandpower analysis and Riemannian covariance methods) for benchmarking purposes.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000206","concept_doi":"10.82901/nemar.nm000206","latest_version_doi":"10.82901/nemar.nm000206.v1.0.0","created_at":"2026-03-24 01:52:47","updated_at":"2026-07-10 21:55:41","zenodo_concept_id":"20518218","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Neuroergonomic 2021 dataset\",\n  \"description\": \"A multi-session EEG dataset acquired from 15 healthy participants performing resting state and graded cognitive tasks (MATB-II at three difficulty levels). The dataset comprises 62-channel EEG recordings at 500 Hz sampling rate designed for passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. Raw EEG data are provided with standardized event annotations using HED 8.4.0 schema and MOABB-compatible feature extraction pipelines (bandpower analysis and Riemannian covariance methods) for benchmarking purposes.\",\n  \"methods_description\": \"EEG data were acquired using a 62-channel ActiCHamp system (Brain Products GmbH) at 500 Hz sampling rate with active Ag/AgCl sensors referenced to Fpz. Standard 10-20 electrode montage was employed with impedance maintained below 25 kOhm. Participants completed two sessions each, performing resting state and three difficulty levels of the MATB-II task (easy, medium, difficult). An auxiliary ECG channel was recorded. Raw data are provided without preprocessing. Feature extraction pipelines compatible with MOABB 1.5.0 are included for benchmarking, including bandpower analysis (theta: 4-8 Hz, alpha: 8-13 Hz) and Riemannian covariance methods.\",\n  \"license\": \"CC-BY-SA-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Marcel F. Hinss\": {\n      \"orcid\": \"0000-0001-9977-4070\"\n    },\n    \"Emilie S. Jahanpour\": {},\n    \"Bertille Somon\": {},\n    \"Lou Pluchon\": {},\n    \"Frédéric Dehais\": {},\n    \"Raphaëlle N. Roy\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"mental workload\"\n    },\n    {\n      \"term\": \"neuroergonomics\"\n    },\n    {\n      \"term\": \"cognitive load\"\n    },\n    {\n      \"term\": \"resting state\"\n    },\n    {\n      \"term\": \"passive BCI\"\n    },\n    {\n      \"term\": \"MATB-II\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-022-01898-y\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.5281/zenodo.6874128\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000206\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=nm000206\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"ERASMUS program\"\n    },\n    {\n      \"funder_name\": \"ANITI (Artificial and Natural Intelligence Toulouse Institute)\"\n    },\n    {\n      \"funder_name\": \"ANITI\",\n      \"award_title\": \"Artificial and Natural Intelligence Toulouse Institute\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.3 GB (31 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"b3c62e30f94e5cf570d2262ecea75ff0027ce41d9080397e8054dde30deab992\"\n}","last_activity_at":"2026-03-24 01:53:06","source":null,"source_id":null,"subject_count":15,"modalities":"eeg","age_min":23.9,"age_max":23.9,"file_size":1310278176,"total_files":31,"tasks":"rstate","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy","license":"CC-BY-SA-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000206-blue)](https://doi.org/10.82901/nemar.nm000206)\n\n# Neuroergonomic 2021 dataset\n\nNeuroergonomic 2021 dataset.\n\n## Dataset Overview\n\n- **Code**: Hinss2021\n- **Paradigm**: rstate\n- **DOI**: 10.1038/s41597-022-01898-y\n- **Subjects**: 15\n- **Sessions per subject**: 2\n- **Events**: rs=1, easy=2, medium=3, diff=4\n- **Trial interval**: [0, 2] s\n- **File format**: set\n\n## Acquisition\n\n- **Sampling rate**: 500.0 Hz\n- **Number of channels**: 62\n- **Channel types**: eeg=62\n- **Channel names**: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT10, FT7, FT8, FT9, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8\n- **Montage**: standard_1020\n- **Hardware**: ActiCHamp (Brain Products Gmbh)\n- **Reference**: Fpz\n- **Sensor type**: active Ag/AgCl\n- **Line frequency**: 50.0 Hz\n- **Impedance threshold**: 25 kOhm\n- **Auxiliary channels**: ecg\n\n## Participants\n\n- **Number of subjects**: 15\n- **Health status**: healthy\n- **Age**: mean=23.9\n- **Gender distribution**: female=11, male=18\n\n## Experimental Protocol\n\n- **Paradigm**: rstate\n- **Number of classes**: 4\n- **Class labels**: rs, easy, medium, diff\n- **Study design**: Passive BCI neuroergonomics dataset with resting state and 3 difficulty levels of MATB-II task (easy, medium, difficult). The MOABB loader provides resting state and MATB conditions only.\n- **Feedback type**: none\n- **Stimulus type**: visual display\n- **Training/test split**: False\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  rs\n    ├─ Experiment-structure\n    └─ Rest\n\n  easy\n    ├─ Experiment-structure\n    └─ Label/easy\n\n  medium\n    ├─ Experiment-structure\n    └─ Label/medium\n\n  diff\n    ├─ Experiment-structure\n    └─ Label/difficult\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: resting_state\n\n## Data Structure\n\n- **Trials**: 90\n- **Trials context**: total\n\n## Preprocessing\n\n- **Data state**: raw\n- **Preprocessing applied**: False\n\n## Signal Processing\n\n- **Classifiers**: MDM, Riemannian\n- **Feature extraction**: Bandpower, Covariance/Riemannian, ICA\n- **Frequency bands**: alpha=[8.0, 13.0] Hz; theta=[4.0, 8.0] Hz\n\n## Cross-Validation\n\n- **Method**: 5-fold\n- **Folds**: 5\n- **Evaluation type**: cross_subject, cross_session, transfer_learning\n\n## Performance (Original Study)\n\n- **Accuracy**: 70.67%\n\n## BCI Application\n\n- **Applications**: neuroergonomics, mental_workload_estimation\n- **Environment**: laboratory\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Cognitive\n- **Type**: Research\n\n## Documentation\n\n- **DOI**: 10.1038/s41597-022-01898-y\n- **License**: CC-BY-SA-4.0\n- **Investigators**: Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy\n- **Senior author**: Raphaëlle N. Roy\n- **Contact**: marcel.hinss@isae-supaero.fr\n- **Institution**: ISAE-SUPAERO, Université de Toulouse\n- **Department**: Department of Information Processing and Systems\n- **Address**: Toulouse, France\n- **Country**: FR\n- **Repository**: Zenodo\n- **Data URL**: https://doi.org/10.5281/zenodo.6874128\n- **Publication year**: 2023\n- **Funding**: ERASMUS program; ANITI (Artificial and Natural Intelligence Toulouse Institute)\n- **Ethics approval**: Comité d'Éthique de la Recherche (CER), Université de Toulouse (CER number 2021-342)\n- **Acknowledgements**: This research was supported in part by the ERASMUS program (which funded Mr Hinss' internship), and by ANITI (Artificial and Natural Intelligence Toulouse Institute), Toulouse, France.\n- **How to acknowledge**: Please cite: Hinss et al. (2023). Open multi-session and multi-task EEG cognitive dataset for passive brain-computer interface applications. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y\n\n## References\n\n.. [Hinss2021] M. Hinss, B. Somon, F. Dehais & R. N. Roy (2021) Open EEG Datasets for Passive Brain-Computer Interface Applications: Lacks and Perspectives. IEEE Neural Engineering Conference.\n\n.. [Hinss2023] M. F. Hinss, et al. (2023) An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y\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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