{"dataset":{"id":"298","dataset_id":"nm000265","name":"Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","description":"A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants performing left and right hand motor imagery in response to visual cues. Recorded at 1000 Hz using a 64-channel EEG montage with standardized electrode placement, this dataset supports brain-computer interface research and analysis of ocular activity patterns during motor imagery paradigms.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000265","concept_doi":"10.82901/nemar.nm000265","latest_version_doi":"10.82901/nemar.nm000265.v1.0.3","created_at":"2026-03-26 18:24:08","updated_at":"2026-08-18 18:20:24","zenodo_concept_id":"20523518","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms\",\n  \"description\": \"A multimodal neuroimaging dataset combining EEG, eye-tracking, and high-speed video recordings from 31 healthy participants performing motor imagery tasks. The dataset comprises 2,520 trials across 63 sessions, with participants performing left and right hand motor imagery in response to visual cues. Recorded at 1000 Hz using a 64-channel EEG montage with standardized electrode placement, this dataset supports brain-computer interface research and analysis of ocular activity patterns during motor imagery paradigms.\",\n  \"methods_description\": \"EEG data were acquired using a Neuroscan Quik-Cap 65-channel system with SynAmps2 amplifier at 1000 Hz sampling rate. The montage followed the standard 1005 electrode placement with 64 EEG channels, 1 EOG channel, and 1 stimulus channel. Reference electrode was placed at the right mastoid (M1) with ground at the forehead. Online high-pass filtering was applied with a 10-second time constant. Participants performed motor imagery tasks in response to visual rectangle cues, with 40 trials per session across up to 3 sessions. Concurrent eye-tracking and high-speed video recordings were obtained to capture ocular activity.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Eva Guttmann-Flury\": {\n      \"orcid\": \"0000-0002-8241-1713\"\n    },\n    \"Xinjun Sheng\": {},\n    \"Xiangyang Zhu\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"eye tracking\"\n    },\n    {\n      \"term\": \"hand motor control\"\n    },\n    {\n      \"term\": \"multimodal neuroimaging\"\n    },\n    {\n      \"term\": \"ocular activity\"\n    },\n    {\n      \"term\": \"video\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-025-04861-9\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000265\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000265\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"36.1 GB (441 files)\"\n  ],\n  \"formats\": [\n    \".avi\",\n    \".bdf\",\n    \".csv\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".txt\",\n    \".xlsx\",\n    \".xml\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"6f967ad75b55aaa5346206479b91e35a773510a5abebcf2528f0700059f1c3f7\"\n}","last_activity_at":"2026-08-16 13:37:30","source":null,"source_id":null,"subject_count":31,"modalities":"eeg","age_min":28.3,"age_max":28.3,"file_size":36094932414,"total_files":1585,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000265-blue)](https://doi.org/10.82901/nemar.nm000265)\n\nGuttmannFlury2025-MI\n====================\n\nEye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025.\n\nDataset Overview\n----------------\n  Code: GuttmannFlury2025-MI\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\nAcquisition\n-----------\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\nParticipants\n------------\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\nExperimental Protocol\n---------------------\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\nHED Event Annotations\n---------------------\n  Schema: 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\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\nParadigm-Specific Parameters\n----------------------------\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\nData Structure\n--------------\n  Trials: 2520\n  Trials context: 63 sessions x 40 trials = 2520 (MI only, default)\n\nBCI Application\n---------------\n  Applications: motor_control\n  Environment: laboratory\n  Online feedback: False\n\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\n\nDocumentation\n-------------\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\nReferences\n----------\nGuttmann-Flury, E., Sheng, X., & Zhu, X. (2025). Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms. Scientific Data, 12, 587. https://doi.org/10.1038/s41597-025-04861-9\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\n\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. 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