{"success":true,"database":"eegdash","data":{"_id":"69d16e06897a7725c66f4ce1","dataset_id":"nm000343","associated_paper_doi":null,"authors":["Marcel F. Hinss","Emilie S. Jahanpour","Bertille Somon","Lou Pluchon","Frédéric Dehais","Raphaëlle N. Roy"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1038/s41597-022-01898-y","datatypes":["eeg"],"demographics":{"subjects_count":15,"ages":[23,23,23,23,23,23,23,23,23,23,23,23,23,23,23],"age_min":23,"age_max":23,"age_mean":23.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/nm000343","osf_url":null,"github_url":null,"paper_url":null},"funding":["ERASMUS program","ANITI (Artificial and Natural Intelligence Toulouse Institute)"],"ingestion_fingerprint":"6abed918b86c59d6b8abd30c714333a64904868cd7ffc133a5f1dfe623e8baeb","license":"CC-BY-SA-4.0","n_contributing_labs":null,"name":"Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications","readme":"Hinss2021\n=========\nNeuroergonomic 2021 dataset.\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\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\nParticipants\n------------\n  Number of subjects: 15\n  Health status: healthy\n  Age: mean=23.9\n  Gender distribution: female=11, male=18\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\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  rs\n    ├─ Experiment-structure\n    └─ Rest\n  easy\n    ├─ Experiment-structure\n    └─ Label/easy\n  medium\n    ├─ Experiment-structure\n    └─ Label/medium\n  diff\n    ├─ Experiment-structure\n    └─ Label/difficult\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: resting_state\nData Structure\n--------------\n  Trials: 90\n  Trials context: total\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\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\nCross-Validation\n----------------\n  Method: 5-fold\n  Folds: 5\n  Evaluation type: cross_subject, cross_session, transfer_learning\nPerformance (Original Study)\n----------------------------\n  Accuracy: 70.67%\nBCI Application\n---------------\n  Applications: neuroergonomics, mental_workload_estimation\n  Environment: laboratory\nTags\n----\n  Pathology: Healthy\n  Modality: Cognitive\n  Type: Research\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\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.. [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\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":["1","2"],"size_bytes":1310931618,"source":"nemar","storage":{"backend":"s3","base":"s3://openneuro.org/nm000343","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["rstate"],"timestamps":{"digested_at":"2026-04-22T12:52:29.691897+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":30,"computed_title":"Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications","nchans_counts":[{"val":61,"count":30}],"sfreq_counts":[{"val":500.0,"count":30}],"stats_computed_at":"2026-04-22T23:16:00.314668+00:00","total_duration_s":14309.94,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"787c2b409577021e","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":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot by paradigm/style is the resting-state datasets (e.g., \"A Resting-state EEG Dataset for Sleep Deprivation\" labeled Healthy/Resting State/Resting-state). That example supports using Pathology=Healthy when participants are healthy and using Resting-state only when there is no task. In contrast, this dataset explicitly includes an active (though passive-BCI) workload manipulation via MATB-II with a \"visual display\", so few-shot conventions suggest Modality should follow the stimulus channel (Visual) rather than response mechanics. For Type, the few-shot digit-span example maps cognitive-load manipulation tied to working memory to Type=Memory; here the construct is mental workload/attention demand (not memory span), guiding a Type closer to Attention (runner-up: Other).","metadata_analysis":"Key participant/pathology facts: (1) \"Health status: healthy\". (2) \"Tags ---- Pathology: Healthy\".\nKey task/modality facts: (1) \"Passive BCI neuroergonomics dataset with resting state and 3 difficulty levels of MATB-II task (easy, medium, difficult).\" (2) \"Stimulus type: visual display\".\nKey construct/type facts: (1) \"Applications: neuroergonomics, mental_workload_estimation\". (2) \"3 difficulty levels of MATB-II task (easy, medium, difficult)\" indicating manipulated workload/attention demand.","paper_abstract_analysis":"No useful paper information. (Only a DOI is provided; no abstract text included in the supplied metadata.)","evidence_alignment_check":"Pathology: Metadata says participants are healthy (\"Health status: healthy\", \"Tags ... Pathology: Healthy\"). Few-shot pattern suggests labeling as Healthy when explicitly stated. ALIGN.\nModality: Metadata says \"Stimulus type: visual display\" and includes MATB-II task with difficulty levels; also includes a resting-state condition. Few-shot pattern suggests Modality tracks stimulus channel, and Resting State is reserved for pure no-task datasets. PARTIAL CONFLICT due to mixed conditions; choose Visual because the task portion has explicit visual stimuli and is central to workload manipulation.\nType: Metadata says application is \"mental_workload_estimation\" within a neuroergonomics passive BCI and includes multiple difficulty levels. Few-shot pattern suggests mapping primary cognitive construct (e.g., load/working memory -> Memory in digit span). Here the construct is workload/attentional demand rather than memory, so Attention best fits; runner-up Other. ALIGN (construct-focused mapping), though the exact label is an inference.","decision_summary":"Top-2 candidates and final choices:\n1) Pathology: (a) Healthy — supported by \"Health status: healthy\" and \"Tags ... Pathology: Healthy\"; (b) Unknown — not supported. Winner: Healthy. Alignment: aligned.\n2) Modality: (a) Visual — supported by \"Stimulus type: visual display\" and MATB-II visual task difficulty manipulation; (b) Resting State — supported by inclusion of \"resting state\" condition. Winner: Visual because dataset is not purely resting-state and explicitly uses a visual display for the workload task. Alignment: mixed but resolved by stimulus-dominant convention.\n3) Type: (a) Attention — supported by \"mental_workload_estimation\" and difficulty-level manipulation implying attentional/workload demand; (b) Other — possible if workload is treated as uncategorized. Winner: Attention as closest allowed construct label. Alignment: mostly aligned; mapping workload->Attention is a justified inference.\nConfidence justification: Pathology high due to multiple explicit statements. Modality moderate-high due to explicit visual stimulus evidence but mixed with resting condition. Type moderate due to explicit workload aim but categorical mapping requires inference."}},"canonical_name":null,"name_confidence":0.72,"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":"Hinss2021"}}