{"dataset":{"id":"191","dataset_id":"nm000160","name":"Multi-joint upper-limb MI dataset from Yi et al. 2025","description":"A multi-joint upper-limb motor imagery EEG dataset comprising 18 healthy subjects performing eight distinct imagery tasks involving hand, wrist, elbow, and shoulder movements. The dataset contains 320 trials per subject acquired at 1000 Hz using 62-channel EEG with visual cue-based paradigm, designed for brain-computer interface research and motor rehabilitation applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000160","concept_doi":"10.82901/nemar.nm000160","latest_version_doi":"10.82901/nemar.nm000160.v1.0.2","created_at":"2026-03-23 00:27:26","updated_at":"2026-08-18 18:15:08","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Multi-joint upper-limb MI dataset from Yi et al. 2025\",\n  \"description\": \"A multi-joint upper-limb motor imagery EEG dataset comprising 18 healthy subjects performing eight distinct imagery tasks involving hand, wrist, elbow, and shoulder movements. The dataset contains 320 trials per subject acquired at 1000 Hz using 62-channel EEG with visual cue-based paradigm, designed for brain-computer interface research and motor rehabilitation applications.\",\n  \"methods_description\": \"EEG data were acquired using a Neuroscan SynAmps2 system at 1000 Hz sampling rate with 62 channels arranged in the standard 1005 montage, referenced to the left mastoid. Participants performed eight motor imagery tasks (hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, shoulder flexion/extension) in response to visual cues and text prompts. Each session consisted of 8 blocks containing 40 trials (5 per class), totaling 320 trials per subject with 4-second trial intervals.\",\n  \"license\": \"CC-BY-NC-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Weibo Yi\": {},\n    \"Jiaming Chen\": {\n      \"orcid\": \"0000-0002-7162-6903\"\n    },\n    \"Dan Wang\": {},\n    \"Xinkang Hu\": {},\n    \"Meng Xu\": {},\n    \"Fangda Li\": {},\n    \"Shuhan Wu\": {},\n    \"Jin Qian\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"upper limb\"\n    },\n    {\n      \"term\": \"motor rehabilitation\"\n    },\n    {\n      \"term\": \"event-related desynchronization\"\n    },\n    {\n      \"term\": \"multi-class classification\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-025-05286-0\",\n      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per session**: 8\n- **File format**: CNT\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 62\n- **Channel types**: eeg=62\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, CB1, O1, Oz, O2, CB2\n- **Montage**: standard_1005\n- **Hardware**: Neuroscan SynAmps2\n- **Reference**: left mastoid (M1)\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 18\n- **Health status**: healthy\n- **Age**: min=22, max=27\n- **Gender distribution**: female=10, male=8\n- **Handedness**: right\n- **BCI experience**: naive\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 8\n- **Class labels**: hand_open_close, wrist_flex_ext, wrist_abd_add, elbow_pron_sup, elbow_flex_ext, shoulder_pron_sup, shoulder_abd_add, shoulder_flex_ext\n- **Trial duration**: 4.0 s\n- **Study design**: 8-class multi-joint upper-limb MI. 8 blocks of 40 trials (5 per class), 320 total trials per subject.\n- **Feedback type**: none\n- **Stimulus type**: video + text\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: cue-based\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  hand_open_close\n    ├─ Sensory-event\n    └─ Label/hand_open_close\n\n  wrist_flex_ext\n    ├─ Sensory-event\n    └─ Label/wrist_flex_ext\n\n  wrist_abd_add\n    ├─ Sensory-event\n    └─ Label/wrist_abd_add\n\n  elbow_pron_sup\n    ├─ Sensory-event\n    └─ Label/elbow_pron_sup\n\n  elbow_flex_ext\n    ├─ Sensory-event\n    └─ Label/elbow_flex_ext\n\n  shoulder_pron_sup\n    ├─ Sensory-event\n    └─ Label/shoulder_pron_sup\n\n  shoulder_abd_add\n    ├─ Sensory-event\n    └─ Label/shoulder_abd_add\n\n  shoulder_flex_ext\n    ├─ Sensory-event\n    └─ Label/shoulder_flex_ext\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: hand_open_close, wrist_flex_ext, wrist_abd_add, elbow_pron_sup, elbow_flex_ext, shoulder_pron_sup, shoulder_abd_add, shoulder_flex_ext\n- **Cue duration**: 2.0 s\n- **Imagery duration**: 4.0 s\n\n## Data Structure\n\n- **Trials**: 320\n- **Trials per class**: hand_open_close=40, wrist_flex_ext=40, wrist_abd_add=40, elbow_pron_sup=40, elbow_flex_ext=40, shoulder_pron_sup=40, shoulder_abd_add=40, shoulder_flex_ext=40\n- **Blocks per session**: 8\n- **Trials context**: 8 blocks x 40 trials (5 per class x 8 classes)\n\n## Signal Processing\n\n- **Classifiers**: ShallowConvNet\n- **Feature extraction**: ERSP\n- **Frequency bands**: alpha=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz; bandpass=[4.0, 40.0] Hz\n- **Spatial filters**: CAR\n\n## Cross-Validation\n\n- **Method**: 5-fold\n- **Folds**: 5\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: rehabilitation\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Motor Imagery\n\n## Documentation\n\n- **DOI**: 10.1038/s41597-025-05286-0\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Weibo Yi, Jiaming Chen, Dan Wang, Xinkang Hu, Meng Xu, Fangda Li, Shuhan Wu, Jin Qian\n- **Institution**: Beijing University of Technology\n- **Country**: CN\n- **Data URL**: https://figshare.com/articles/dataset/Data/24123303\n- **Publication year**: 2025\n\n## References\n\nYi, W., Chen, J., Wang, D., et al. (2025). A multi-modal dataset of EEG and fNIRS for motor imagery of multi-types of joints from unilateral upper limb. Scientific Data, 12, 953. https://doi.org/10.1038/s41597-025-05286-0\n\nNotes\n\n.. versionadded:: 1.2.0\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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