{"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":"10.82901/nemar.nm000343","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://nemar.org/dataexplorer/detail/nm000343","osf_url":null,"github_url":null,"paper_url":null},"funding":["ERASMUS program","ANITI (Artificial and Natural Intelligence Toulouse Institute)"],"ingestion_fingerprint":"81ced65ac8db12a4a094e8768bdb9b4bfed278fdca61b004f6d1cbed85811091","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":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000343-blue)](https://doi.org/10.82901/nemar.nm000343)\n# Hinss2021\nNeuroergonomic 2021 dataset.\n## Revision note (version 1.1.0, 2026-10-14)\nThe BIDS recordings were regenerated with the corrected MOABB `Hinss2021` loader ([NeuroTechX/moabb#1270](https://github.com/NeuroTechX/moabb/pull/1270), issue [#663](https://github.com/NeuroTechX/moabb/issues/663)). The released EEGLAB epochs are 2 s long at 250 Hz, but versions up to 1.0.3 of this deposit wrote them with a declared sampling rate of 500 Hz: every duration was halved (a session file lasted 477 s instead of 954 s), every frequency was doubled, and each 2 s event covered two released epochs. The files are now written at 250 Hz and each of the 477 events per session starts on the first sample of its released epoch (duration 1.996 s = 500 samples). Sample values, channels and event labels are unchanged. `sourcedata/` (the original files) is unchanged. See CHANGES.\n## Dataset Overview\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, 1.996] s\n- **File format**: set\n## Acquisition\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## Participants\n- **Number of subjects**: 15\n- **Health status**: healthy\n- **Age**: mean=23.9\n- **Gender distribution**: female=11, male=18\n## Experimental Protocol\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## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\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\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: resting_state\n## Data Structure\n- **Trials**: 90\n- **Trials context**: total\n## Preprocessing\n- **Data state**: raw\n- **Preprocessing applied**: False\n- **Downsampled to**: 250.0 Hz\n## Signal Processing\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## Cross-Validation\n- **Method**: 5-fold\n- **Folds**: 5\n- **Evaluation type**: cross_subject, cross_session, transfer_learning\n## Performance (Original Study)\n- **Accuracy**: 70.67%\n## BCI Application\n- **Applications**: neuroergonomics, mental_workload_estimation\n- **Environment**: laboratory\n## Tags\n- **Pathology**: Healthy\n- **Modality**: Cognitive\n- **Type**: Research\n## Documentation\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## References\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 1.8.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":"Raphaëlle N. Roy","sessions":["1","2"],"size_bytes":4342918471,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000343","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["rstate"],"timestamps":{"digested_at":"2026-10-22T08:58:13.023265+00:00","dataset_created_at":null,"dataset_modified_at":"2026-10-14 19:13:05"},"total_files":225,"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":250.0,"count":30}],"stats_computed_at":"2026-10-22T09:54:07.650159+00:00","total_duration_s":28620.0,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"The resting-state sleep-deprivation example shows that eyes-open or eyes-closed rest maps to No stimulus and Resting-state. The auditory digit-span example shows that cognitive load does not by itself determine Type: its memory task is labeled Memory. Here, the MATB-II difficulty manipulation and stated mental-workload aim support Attention rather than Memory; the resting-state portion remains relevant but is not the sole paradigm.","metadata_analysis":"The readme states \"Health status: healthy\" and \"Tags ... Pathology: Healthy\". It describes \"resting state and 3 difficulty levels of MATB-II task (easy, medium, difficult)\", specifies \"Stimulus type: visual display\", and lists \"Applications: neuroergonomics, mental_workload_estimation\". Thus, the dataset includes both rest and a visually presented, difficulty-varying task. The generated tags \"Modality: Cognitive\" and \"Type: Research\" are not allowed labels and do not identify the sensory channel or cognitive construct.","paper_abstract_analysis":"No paper abstract is provided. The readme cites a paper on cross-session mental-workload estimation, consistent with its stated application.","evidence_alignment_check":"Pathology: metadata says \"Health status: healthy\"; healthy-cohort few-shots suggest Healthy. ALIGN. Modality: metadata says \"Stimulus type: visual display\" but also includes \"resting state\"; visual-task few-shots suggest Visual, while resting-state examples suggest No stimulus for the rest portion. ALIGN for their respective conditions; Visual represents the stimulus-bearing task. Type: metadata says \"mental_workload_estimation\" and describes three MATB-II difficulty levels alongside rest. Few-shots suggest labeling the substantive task aim rather than letting a rest condition determine the whole dataset's Type. ALIGN with Attention as the closest available construct, although workload is not explicitly named attention.","decision_summary":"Pathology—Healthy versus Unknown: \"Health status: healthy\" and \"Pathology: Healthy\" favor Healthy; aligned, confidence 0.8. Modality—Visual versus No stimulus: \"Stimulus type: visual display\" favors Visual for MATB-II; \"resting state\" supports No stimulus only for the accompanying condition. The task stimulus wins; partially aligned, confidence 0.8. Type—Attention versus Resting-state: \"mental_workload_estimation\" and \"3 difficulty levels of MATB-II task\" support studying attentional workload, while \"resting state\" supports the runner-up for one condition. Attention is the stronger dataset-level aim; partially aligned, confidence 0.7. No explicit metadata fact was overridden by a few-shot pattern."}},"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","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":6,"is_oa":true,"oa_status":"gold","source":"paper_resolver"},"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.","ethics_approvals":["Comité d'Éthique de la Recherche (CER), Université de Toulouse (CER number 2021-342)"],"generated_by":[{"CodeURL":"https://github.com/NeuroTechX/moabb","Name":"moabb","Description":"Mother of All BCI Benchmarks","Version":"1.8.0 (develop 723cca6fe + fix/braindecode-loader-663, NeuroTechX/moabb#1270)"}],"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","references_and_links":["https://doi.org/10.5281/zenodo.6874128"],"source_datasets":[{"DOI":"10.1038/s41597-022-01898-y","URL":"https://doi.org/10.5281/zenodo.6874128"},{"DOI":"doi:10.1038/s41597-022-01898-y"}],"bad_channels_info":null,"n_records":30}}