{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cb4","dataset_id":"nm000227","associated_paper_doi":null,"authors":["Eva Guttmann-Flury","Xinjun Sheng","Xiangyang Zhu"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":31,"ages":[28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28],"age_min":28,"age_max":28,"age_mean":28.0,"species":null,"sex_distribution":{"f":11,"m":20},"handedness_distribution":{"r":24,"l":2}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000227","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"4d94fc5bee19c86aa2aa7d601defd40e77e20705870557ea0fa81cc3c57e962e","license":"CC0","n_contributing_labs":null,"name":"Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025","readme":"# Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025\nEye-BCI Motor Execution dataset from Guttmann-Flury et al 2025.\n## Dataset Overview\n- **Code**: GuttmannFlury2025-ME\n- **Paradigm**: imagery\n- **DOI**: 10.1038/s41597-025-04861-9\n- **Subjects**: 31\n- **Sessions per subject**: 3\n- **Events**: left_hand=1, right_hand=2\n- **Trial interval**: [0, 4] s\n- **File format**: BDF\n## Acquisition\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 66\n- **Channel types**: eeg=64, eog=1, stim=1\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, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, O1, OZ, O2, CB1, CB2\n- **Montage**: standard_1005\n- **Hardware**: Neuroscan Quik-Cap 65-ch, SynAmps2\n- **Reference**: right mastoid (M1)\n- **Ground**: forehead\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 50.0 Hz\n- **Online filters**: {'highpass_time_constant_s': 10}\n## Participants\n- **Number of subjects**: 31\n- **Health status**: healthy\n- **Age**: mean=28.3, min=20.0, max=57.0\n- **Gender distribution**: female=11, male=20\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: imagery\n- **Number of classes**: 2\n- **Class labels**: left_hand, right_hand\n- **Trial duration**: 7.5 s\n- **Study design**: Multi-paradigm BCI (MI/ME/SSVEP/P300). MI and ME: 2-class hand grasping, 40 trials/session, up to 3 sessions per subject.\n- **Feedback type**: none\n- **Stimulus type**: visual rectangle cue\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  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_hand, right_hand\n- **Cue duration**: 2.0 s\n- **Imagery duration**: 4.0 s\n## Data Structure\n- **Trials**: 2520\n- **Trials context**: 63 sessions x 40 trials = 2520 (MI only, default)\n## BCI Application\n- **Applications**: motor_control\n- **Environment**: laboratory\n- **Online feedback**: False\n## Tags\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Research\n## Documentation\n- **DOI**: 10.1038/s41597-025-04861-9\n- **License**: CC0\n- **Investigators**: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu\n- **Institution**: Shanghai Jiao Tong University\n- **Country**: CN\n- **Publication year**: 2025\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 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0","1","2"],"size_bytes":5059344081,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000227","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-04-30T14:09:13.206059+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-25T16:25:49Z"},"total_files":63,"computed_title":"Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025","nchans_counts":[{"val":66,"count":63}],"sfreq_counts":[{"val":1000.0,"count":63}],"stats_computed_at":"2026-05-01T13:49:34.645881+00:00","total_duration_s":25536.937,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"e03384e012ecfb80","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Motor"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"Closest match is the few-shot example “EEG Motor Movement/Imagery Dataset” (Schalk et al., BCI2000), which labels motor execution/imagery experiments as Pathology=Healthy and Type=Motor, with Modality=Visual due to screen-based cues (“A target appears on either the left or the right side of the screen...”). The current dataset is also a 2-class left/right hand grasping MI/ME BCI task with explicit visual cues, so the same labeling convention applies. The Parkinson/TBI/dementia few-shots demonstrate that when a clinical diagnosis is explicit it drives Pathology, but here the metadata explicitly states healthy participants.","metadata_analysis":"Population/Pathology facts: (1) “Health status: healthy”; (2) “Subjects: 31”; (3) “## Tags\\n- **Pathology**: Healthy”.\nStimulus modality facts: (1) “Stimulus type: visual rectangle cue”; (2) “Stimulus modalities: visual”; (3) “Primary modality: visual”.\nCognitive/task type facts: (1) “Study design: Multi-paradigm BCI (MI/ME/SSVEP/P300). MI and ME: 2-class hand grasping”; (2) “Detected paradigm: motor_imagery”; (3) “Events: left_hand=1, right_hand=2” and HED annotations show “Agent-action -> Imagine -> Move -> Left/Right, Hand”.","paper_abstract_analysis":"No useful paper information (abstract not provided in metadata).","evidence_alignment_check":"Pathology: Metadata says “Health status: healthy” and tag “Pathology: Healthy”. Few-shot pattern suggests Healthy when recruiting is non-clinical (e.g., Schalk motor imagery dataset). ALIGN.\nModality: Metadata says “Stimulus type: visual rectangle cue” and “Primary modality: visual”. Few-shot motor imagery example also treated screen-cued MI/ME as Visual modality. ALIGN.\nType: Metadata says “MI and ME: 2-class hand grasping” and “Detected paradigm: motor_imagery” with HED “Imagine -> Move -> Hand”. Few-shot motor imagery example maps such paradigms to Type=Motor. ALIGN.","decision_summary":"Top-2 candidates (with head-to-head choice):\n1) Pathology: Healthy vs Unknown. Healthy wins because metadata explicitly states “Health status: healthy”, plus “Tags… Pathology: Healthy”, and participant section lists healthy cohort.\n2) Modality: Visual vs Motor. Visual wins because Modality is defined by stimulus/input channel, and metadata explicitly states “Stimulus type: visual rectangle cue”, “Stimulus modalities: visual”, and “Primary modality: visual”. Motor is the response/imagery domain, handled under Type.\n3) Type: Motor vs Perception. Motor wins because the paradigm is motor imagery/execution (“MI and ME: 2-class hand grasping”, “Detected paradigm: motor_imagery”, HED “Imagine -> Move -> Hand”), not sensory discrimination.\nConfidence justification: High confidence supported by multiple explicit metadata quotes per category and strong alignment with the Schalk motor imagery few-shot convention."}},"canonical_name":null,"name_confidence":0.62,"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":"GuttmannFlury2025_Eye"}}