{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c66","dataset_id":"nm000125","associated_paper_doi":null,"authors":["Young-Eun Lee","Gi-Hwan Shin","Minji Lee","Seong-Whan Lee"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":23,"ages":[28,22,29,21,26,27,24,24,22,23,32,24,26,24,21,25,23,24,24,22,28,19,27],"age_min":19,"age_max":32,"age_mean":24.565217391304348,"species":null,"sex_distribution":{"f":9,"m":14},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000125","osf_url":null,"github_url":null,"paper_url":null},"funding":["IITP No. 2017-0-00451","IITP No. 2015-0-00185","IITP No. 2019-0-00079"],"ingestion_fingerprint":"d1e9845085e0ec6904a12a445cc81d95275ca59bde538f46f575fe39a9d8bb09","license":"CC BY 4.0","n_contributing_labs":null,"name":"Lee2021 – SSVEP paradigm of the Mobile BCI dataset","readme":"# SSVEP paradigm of the Mobile BCI dataset\nSSVEP paradigm of the Mobile BCI dataset.\n## Dataset Overview\n- **Code**: Lee2021Mobile-SSVEP\n- **Paradigm**: ssvep\n- **DOI**: 10.1038/s41597-021-01094-4\n- **Subjects**: 23\n- **Sessions per subject**: 4\n- **Events**: 5.45=11, 8.57=12, 12.0=13\n- **Trial interval**: [0, 5] s\n- **File format**: BrainVision\n## Acquisition\n- **Sampling rate**: 100.0 Hz\n- **Number of channels**: 73\n- **Channel types**: eeg=73\n- **Montage**: standard_1005\n- **Hardware**: BrainAmp (Brain Product GmbH)\n- **Reference**: FCz\n- **Ground**: Fpz\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 60.0 Hz\n- **Impedance threshold**: 50 kOhm\n- **Electrode material**: Ag/AgCl\n- **Auxiliary channels**: EOG (4 ch, vertical, horizontal)\n## Participants\n- **Number of subjects**: 23\n- **Health status**: healthy\n- **Age**: mean=24.5, std=2.9, min=19, max=32\n- **Gender distribution**: male=13, female=10\n## Experimental Protocol\n- **Paradigm**: ssvep\n- **Number of classes**: 3\n- **Class labels**: 5.45, 8.57, 12.0\n- **Trial duration**: 5.0 s\n- **Study design**: BCI during motion (standing/walking/running)\n- **Stimulus type**: visual flicker\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\n  5.45\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/5_45\n  8.57\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8_57\n  12.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12_0\n```\n## Signal Processing\n- **Classifiers**: rLDA, CCA\n- **Feature extraction**: power_over_time_intervals, CCA\n- **Frequency bands**: delta=[0.5, 3.5] Hz; theta=[3.5, 7.5] Hz; alpha=[7.5, 12.5] Hz; beta=[12.5, 30.0] Hz\n## Cross-Validation\n- **Method**: holdout\n- **Evaluation type**: within_subject\n## BCI Application\n- **Applications**: mobile_BCI\n- **Environment**: treadmill\n## Tags\n- **Pathology**: healthy\n- **Modality**: visual\n- **Type**: perception\n## Documentation\n- **DOI**: 10.1038/s41597-021-01094-4\n- **License**: CC BY 4.0\n- **Investigators**: Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee\n- **Senior author**: Seong-Whan Lee\n- **Institution**: Korea University\n- **Country**: KR\n- **Repository**: OSF\n- **Data URL**: https://osf.io/r7s9b/\n- **Publication year**: 2021\n- **Funding**: IITP No. 2017-0-00451; IITP No. 2015-0-00185; IITP No. 2019-0-00079\n- **Ethics approval**: Institutional Review Board of Korea University, KUIRB-2019-0194-01\n- **Keywords**: SSVEP, ERP, mobile BCI, ear-EEG, locomotion\n## References\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":["2","3","4","5"],"size_bytes":1407985668,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000125","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["ssvep"],"timestamps":{"digested_at":"2026-04-30T14:08:34.730936+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-29T22:38:24Z"},"total_files":85,"computed_title":"Lee2021 – SSVEP paradigm of the Mobile BCI dataset","nchans_counts":[{"val":73,"count":84},{"val":46,"count":1}],"sfreq_counts":[{"val":100.0,"count":85}],"stats_computed_at":"2026-05-01T13:49:34.644831+00:00","total_duration_s":48009.42,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"7c8a9a2647bb9386","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.85,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example by stimulus modality and sensory-paradigm framing is: (1) \"Meta-rdk: Preprocessed EEG data\" labeled Visual + Perception, where a visual discrimination paradigm is mapped to Type=Perception (sensory/perceptual processing with visual stimuli). This guides treating a visual SSVEP flicker paradigm primarily as a sensory/perceptual dataset rather than motor, even if participants may move. A second partial analog is \"Subcortical responses to music and speech...\" labeled Auditory + Perception, showing that stimulus-driven evoked-response paradigms are conventionally mapped to Type=Perception.","metadata_analysis":"Key metadata facts:\n- Population/health: \"Health status: healthy\" and \"Number of subjects: 23\".\n- Paradigm: \"Paradigm: ssvep\" and \"SSVEP paradigm of the Mobile BCI dataset.\".\n- Stimulus/modality: \"Stimulus type: visual flicker\", \"Stimulus modalities: visual\", and HED: \"Visual-presentation\" under events (5.45/8.57/12.0).\n- Context: \"Study design: BCI during motion (standing/walking/running)\" (movement is an experimental context, but the driving input is still visual flicker).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"Health status: healthy\".\n- Few-shot pattern suggests: datasets explicitly stating healthy participants are labeled \"Healthy\".\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"Stimulus type: visual flicker\" and \"Stimulus modalities: visual\" (also HED: \"Visual-presentation\").\n- Few-shot pattern suggests: visual stimulus paradigms map to Modality=\"Visual\".\n- Alignment: ALIGN.\n\nType:\n- Metadata says: \"Paradigm: ssvep\" with visual flicker class labels/frequencies (5.45, 8.57, 12.0), implying steady-state visually evoked responses; also includes BCI classifiers (\"CCA\").\n- Few-shot pattern suggests: stimulus-evoked sensory paradigms (visual discrimination; auditory encoding) are mapped to Type=\"Perception\".\n- Alignment: ALIGN (despite \"BCI during motion\", the studied signal is visually evoked/SSVEP rather than movement execution as the cognitive focus).","decision_summary":"Top-2 comparative selections:\n\nPathology candidates:\n1) Healthy (selected) — Evidence: \"Health status: healthy\"; participants described as healthy with age/gender summary.\n2) Unknown — would apply only if no recruitment health info; not the case.\nAlignment: aligned with few-shot conventions. Confidence basis: 2+ explicit metadata statements (health status + participant description).\n\nModality candidates:\n1) Visual (selected) — Evidence: \"Stimulus type: visual flicker\"; \"Stimulus modalities: visual\"; HED tags include \"Visual-presentation\".\n2) Motor — possible because \"BCI during motion (standing/walking/running)\", but movement is not the stimulus channel.\nAlignment: aligned with few-shot conventions. Confidence basis: 3 explicit modality indicators.\n\nType candidates:\n1) Perception (selected) — Evidence: \"Paradigm: ssvep\"; \"Stimulus type: visual flicker\"; frequency-labeled stimulus classes (\"Number of classes: 3\", \"Class labels: 5.45, 8.57, 12.0\") strongly indicate visually evoked sensory responses.\n2) Attention — plausible because SSVEP BCIs often require attending to a flickering target, but the metadata emphasizes evoked visual flicker/SSVEP paradigm rather than attention manipulation as the primary construct.\nAlignment: aligned with few-shot conventions (visual stimulus-driven paradigms -> Perception). Confidence basis: 2+ explicit paradigm/stimulus quotes."}},"canonical_name":null,"name_confidence":0.66,"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":"Lee2021_SSVEP"}}