{"dataset":{"id":"378","dataset_id":"nm000343","name":"Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications","description":"This open-access EEG dataset comprises multi-session, multi-task recordings from 15 healthy participants performing resting state and graded difficulty levels of the MATB-II task. Acquired at 500 Hz using 62 active electrodes, the dataset includes 90 trials per participant across two sessions and is designed to support passive brain-computer interface research and mental workload estimation in neuroergonomic applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000343","concept_doi":"10.82901/nemar.nm000343","latest_version_doi":"10.82901/nemar.nm000343.v1.0.3","created_at":"2026-03-28 17:33:17","updated_at":"2026-08-18 18:23:01","zenodo_concept_id":"20527487","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications\",\n  \"description\": \"This open-access EEG dataset comprises multi-session, multi-task recordings from 15 healthy participants performing resting state and graded difficulty levels of the MATB-II task. Acquired at 500 Hz using 62 active electrodes, the dataset includes 90 trials per participant across two sessions and is designed to support passive brain-computer interface research and mental workload estimation in neuroergonomic applications.\",\n  \"methods_description\": \"EEG data were acquired using an ActiCHamp system (Brain Products GmbH) with 62 active Ag/AgCl electrodes in a standard 10-20 montage, referenced to Fpz, at a sampling rate of 500 Hz. Participants completed resting state and three difficulty levels of the MATB-II task (easy, medium, difficult) across two sessions. Impedance was maintained below 25 kOhm, and auxiliary ECG recordings were obtained.\",\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. 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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.nm000343-blue)](https://doi.org/10.82901/nemar.nm000343)\n\nHinss2021\n=========\n\nNeuroergonomic 2021 dataset.\n\nDataset 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\nAcquisition\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\nParticipants\n------------\n  Number of subjects: 15\n  Health status: healthy\n  Age: mean=23.9\n  Gender distribution: female=11, male=18\n\nExperimental 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\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\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\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: resting_state\n\nData Structure\n--------------\n  Trials: 90\n  Trials context: total\n\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\n\nSignal 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\nCross-Validation\n----------------\n  Method: 5-fold\n  Folds: 5\n  Evaluation type: cross_subject, cross_session, transfer_learning\n\nPerformance (Original Study)\n----------------------------\n  Accuracy: 70.67%\n\nBCI Application\n---------------\n  Applications: neuroergonomics, mental_workload_estimation\n  Environment: laboratory\n\nTags\n----\n  Pathology: Healthy\n  Modality: Cognitive\n  Type: Research\n\nDocumentation\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\nReferences\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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