{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c65","dataset_id":"nm000124","associated_paper_doi":null,"authors":["Yuheng Han","Yufeng Ke","Ruiyan Wang","Tao Wang","Dong Ming"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":24,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000124","osf_url":null,"github_url":null,"paper_url":null},"funding":["National Key Research and Development Program of China (Grant 2021YFF1200603)","National Natural Science Foundation of China (Grants 62276184, 61806141)"],"ingestion_fingerprint":"f14f28c8ce97ce79e5833eccc5f135f60e49bdda0dcc1e7125579b6d6722fd29","license":"CC BY 4.0","n_contributing_labs":null,"name":"Han2024 – SSVEP fatigue dataset with two frequency paradigms","readme":"# SSVEP fatigue dataset with two frequency paradigms\nSSVEP fatigue dataset with two frequency paradigms.\n## Dataset Overview\n- **Code**: Han2024Fatigue\n- **Paradigm**: ssvep\n- **DOI**: 10.1109/TNSRE.2024.3380635\n- **Subjects**: 24\n- **Sessions per subject**: 2\n- **Events**: 8=1, 8.5=2, 9=3, 9.5=4, 10=5, 10.5=6, 11=7, 11.5=8, 12=9, 12.5=10, 13=11, 13.5=12, 14=13, 14.5=14, 15=15, 15.5=16, 25.5=17, 26=18, 26.5=19, 27=20, 27.5=21, 28=22, 28.5=23, 29=24, 29.5=25, 30=26, 30.5=27, 31=28, 31.5=29, 32=30, 32.5=31, 33=32\n- **Trial interval**: [0.14, 2.14] s\n- **File format**: MAT\n## Acquisition\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 64\n- **Channel types**: eeg=64\n- **Channel names**: Fp1, Fpz, Fp2, AF3, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, M1, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, M2, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2\n- **Montage**: standard_1005\n- **Hardware**: Synamps2 (Neuroscan)\n- **Reference**: Cz\n- **Ground**: midway between Fz and FPz\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {'bandpass_hz': [0.15, 200.0]}\n- **Impedance threshold**: 10 kOhm\n## Participants\n- **Number of subjects**: 24\n- **Health status**: healthy\n- **Age**: min=18, max=26\n- **Gender distribution**: male=12, female=12\n## Experimental Protocol\n- **Paradigm**: ssvep\n- **Task type**: gaze-shifting\n- **Number of classes**: 32\n- **Class labels**: 8, 8.5, 9, 9.5, 10, 10.5, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5, 15, 15.5, 25.5, 26, 26.5, 27, 27.5, 28, 28.5, 29, 29.5, 30, 30.5, 31, 31.5, 32, 32.5, 33\n- **Trial duration**: 2.0 s\n- **Feedback type**: none\n- **Stimulus type**: JFPM visual flicker\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: True\n## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\n  8\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8\n  8.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8_5\n  9\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/9\n  9.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/9_5\n  10\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10\n  10.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10_5\n  11\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11\n  11.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11_5\n  12\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12\n  12.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12_5\n  13\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/13\n  13.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/13_5\n  14\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/14\n  14.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/14_5\n  15\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/15\n  15.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/15_5\n  25.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/25_5\n  26\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/26\n  26.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/26_5\n  27\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/27\n  27.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/27_5\n  28\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/28\n  28.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/28_5\n  29\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/29\n  29.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/29_5\n  30\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/30\n  30.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/30_5\n  31\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/31\n  31.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/31_5\n  32\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/32\n  32.5\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/32_5\n  33\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/33\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: ssvep\n- **Stimulus frequencies**: [8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0] Hz\n- **Frequency resolution**: 0.5 Hz\n## Data Structure\n- **Trials**: 960 per frequency band (16 targets x 60 blocks)\n- **Blocks per session**: 60\n- **Trials context**: 6 training + 24 fatigue blocks per frequency condition\n## Preprocessing\n- **Data state**: epoched\n## Signal Processing\n- **Classifiers**: TRCA\n- **Spatial filters**: TRCA\n## BCI Application\n- **Environment**: lab\n- **Online feedback**: False\n## Tags\n- **Pathology**: healthy\n- **Modality**: visual\n- **Type**: perception\n## Documentation\n- **DOI**: 10.1109/TNSRE.2024.3380635\n- **License**: CC BY 4.0\n- **Investigators**: Yuheng Han, Yufeng Ke, Ruiyan Wang, Tao Wang, Dong Ming\n- **Senior author**: Dong Ming\n- **Institution**: Tianjin University\n- **Department**: Academy of Medical Engineering and Translational Medicine, Tianjin University\n- **Country**: CN\n- **Repository**: Zenodo\n- **Data URL**: https://zenodo.org/records/10507229\n- **Publication year**: 2024\n- **Funding**: National Key Research and Development Program of China (Grant 2021YFF1200603); National Natural Science Foundation of China (Grants 62276184, 61806141)\n- **Ethics approval**: Research Ethics Committee of Tianjin University\n- **Keywords**: SSVEP, BCI, fatigue, dynamic stopping, EEG\n## References\nY. Han, Y. Ke, R. Wang, T. Wang, and D. Ming, \"Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy,\" IEEE Trans. Neural Syst. Rehab. Eng., vol. 32, pp. 1407-1415, 2024. DOI: 10.1109/TNSRE.2024.3380635\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 1.4.3 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0","1"],"size_bytes":18297653917,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000124","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["ssvep"],"timestamps":{"digested_at":"2026-04-30T14:08:34.523542+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-29T22:38:30Z"},"total_files":48,"computed_title":"Han2024 – SSVEP fatigue dataset with two frequency paradigms","nchans_counts":[{"val":64,"count":48}],"sfreq_counts":[{"val":1000.0,"count":48}],"stats_computed_at":"2026-05-01T13:49:34.644811+00:00","total_duration_s":71423.952,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"c209a6c7127f99d4","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot conventions are the healthy, visually-driven paradigms where the stimulus channel is visual and the construct is primarily sensory/perceptual (e.g., the schizophrenia dataset uses a \"visual discrimination task\" and is labeled Modality=Visual, Type=Perception). While this dataset is SSVEP-BCI (not a discrimination task per se), the few-shot examples support mapping visually evoked paradigms to Modality=Visual and typically Type=Perception when the main manipulation is sensory stimulation/evoked responses rather than learning/memory/motor execution. For Pathology, several examples show that when metadata states participants are healthy, label Pathology=Healthy.","metadata_analysis":"Key metadata facts:\n- Population: \"Health status: healthy\" and \"Number of subjects: 24\" (ages 18–26).\n- Stimulus/modality: \"Stimulus type: JFPM visual flicker\", \"Stimulus modalities: visual\", and the paradigm is explicitly \"Paradigm: ssvep\" with many \"Stimulus frequencies\".\n- Study goal context: keywords include \"SSVEP, BCI, fatigue, dynamic stopping\" and the title is \"SSVEP fatigue dataset with two frequency paradigms\".\n- Type tag provided in README: \"Type: perception\" (in the Tags section).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"Health status: healthy\" and Tags include \"Pathology: healthy\".\n- Few-shot pattern suggests: when participants are healthy controls/volunteers, use Pathology=Healthy.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"Stimulus type: JFPM visual flicker\", \"Stimulus modalities: visual\", and HED annotations are \"Visual-presentation\".\n- Few-shot pattern suggests: visual stimuli -> Modality=Visual.\n- Alignment: ALIGN.\n\nType:\n- Metadata says: \"Paradigm: ssvep\" with visual flicker frequencies and explicitly lists \"Type: perception\" (Tags section).\n- Few-shot pattern suggests: stimulus-evoked sensory paradigms are typically Type=Perception unless the primary construct is clearly memory/learning/motor/rest.\n- Alignment: ALIGN (though 'fatigue' could also suggest Attention; metadata-provided type supports Perception).","decision_summary":"Top-2 candidates per category and selection:\n\nPathology:\n1) Healthy — Evidence: \"Health status: healthy\"; \"Pathology: healthy\" (Tags).\n2) Unknown — Only if health status were not explicit.\nWinner: Healthy (explicitly stated). Alignment: aligned with few-shot convention.\n\nModality:\n1) Visual — Evidence: \"Stimulus type: JFPM visual flicker\"; \"Stimulus modalities: visual\"; HED: \"Visual-presentation\".\n2) Other — Only if the stimulus modality were unclear.\nWinner: Visual (multiple explicit statements). Alignment: aligned with few-shot convention.\n\nType:\n1) Perception — Evidence: \"Paradigm: ssvep\"; \"Stimulus type: ... visual flicker\" / many flicker frequencies; explicit tag \"Type: perception\".\n2) Attention — Plausible due to \"fatigue\" and performance under fatigue (\"Enhancing SSVEP-BCI Performance Under Fatigue State...\").\nWinner: Perception (SSVEP is primarily a visually evoked/perceptual stimulation paradigm and metadata explicitly tags it as perception). Alignment: aligned with few-shot convention.\n\nConfidence justifications (by evidence count):\n- Pathology 0.9: 2 explicit metadata statements (health status + pathology tag) and strong few-shot consistency.\n- Modality 0.9: 3 explicit modality cues (stimulus type, stimulus modalities, HED visual-presentation) and strong few-shot consistency.\n- Type 0.8: 1 explicit type tag (\"Type: perception\") plus strong paradigm/stimulus support; runner-up (Attention) plausible but less directly indicated as the primary construct."}},"canonical_name":null,"name_confidence":0.74,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Han2024"}}