{"dataset":{"id":"260","dataset_id":"nm000227","name":"Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025","description":"A motor imagery EEG dataset comprising 31 healthy participants performing left and right hand grasping imagery tasks across three sessions. The dataset includes 2,520 trials recorded at 1000 Hz using a 64-channel Neuroscan system with standardized electrode montage, designed for brain-computer interface research and motor control applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000227","concept_doi":"10.82901/nemar.nm000227","latest_version_doi":"10.82901/nemar.nm000227.v1.0.2","created_at":"2026-03-25 16:23:02","updated_at":"2026-08-18 21:18:13","zenodo_concept_id":"20519490","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025\",\n  \"description\": \"A motor imagery EEG dataset comprising 31 healthy participants performing left and right hand grasping imagery tasks across three sessions. The dataset includes 2,520 trials recorded at 1000 Hz using a 64-channel Neuroscan system with standardized electrode montage, designed for brain-computer interface research and motor control applications.\",\n  \"methods_description\": \"EEG data were acquired using a Neuroscan Quik-Cap 65-channel system with SynAmps2 amplifier at 1000 Hz sampling rate. Sixty-four EEG channels were referenced to the right mastoid with forehead ground. Ag/AgCl sensors were used with standard 1005 montage. Online high-pass filtering (10 s time constant) was applied. Participants performed motor imagery of left and right hand grasping in response to visual rectangle cues, with 40 trials per session across up to 3 sessions.\",\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\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"motor control\"\n    },\n    {\n      \"term\": \"hand imagery\"\n    },\n    {\n      \"term\": \"healthy volunteers\"\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/nm000227\",\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/nm000227\",\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    \"32.5 GB (379 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\": \"e58f1bbf7d6c9844d280cc41cc6570d30913ec1458ea061dff4be34edaecc018\"\n}","last_activity_at":"2026-08-16 13:34:20","source":null,"source_id":null,"subject_count":31,"modalities":"eeg","age_min":28.3,"age_max":28.3,"file_size":32533444780,"total_files":1208,"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.nm000227-blue)](https://doi.org/10.82901/nemar.nm000227)\n\n# Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025\n\nEye-BCI Motor Execution dataset from Guttmann-Flury et al 2025.\n\n## Dataset Overview\n\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\n## Acquisition\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\n## Participants\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\n## Experimental 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\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\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\n```\n## Paradigm-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\n## Data Structure\n\n- **Trials**: 2520\n- **Trials context**: 63 sessions x 40 trials = 2520 (MI only, default)\n\n## BCI Application\n\n- **Applications**: motor_control\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Research\n\n## Documentation\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\n## References\n\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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