{"dataset":{"id":"198","dataset_id":"nm000167","name":"Motor imagery dataset from Ma et al. 2020","description":"This dataset contains 62-channel EEG recordings from 25 healthy, right-handed subjects performing kinesthetic motor imagery of the right hand versus right elbow, a within-limb discrimination paradigm. Each subject completed 15 sessions over 3 days, yielding 600 trials total, designed to study brain-computer interface (BCI) decoding of different joint movements from the same limb. The dataset supports research into motor rehabilitation and prosthetic control applications using EEG-based BCI systems.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000167","concept_doi":"10.82901/nemar.nm000167","latest_version_doi":"10.82901/nemar.nm000167.v1.0.2","created_at":"2026-03-23 13:02:39","updated_at":"2026-08-18 22:07:17","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Motor imagery dataset from Ma et al. 2020\",\n  \"description\": \"This dataset contains 62-channel EEG recordings from 25 healthy, right-handed subjects performing kinesthetic motor imagery of the right hand versus right elbow, a within-limb discrimination paradigm. Each subject completed 15 sessions over 3 days, yielding 600 trials total, designed to study brain-computer interface (BCI) decoding of different joint movements from the same limb. The dataset supports research into motor rehabilitation and prosthetic control applications using EEG-based BCI systems.\",\n  \"methods_description\": \"EEG was recorded at 1000 Hz using a Neuroscan SynAmps2 system with 62 channels arranged per the standard_1005 montage, plus horizontal and vertical EOG channels. Subjects performed kinesthetic motor imagery of right hand or right elbow movements following a visual cue, with each trial lasting 4 seconds. Data were organized into 15 sessions (5 sessions/day over 3 days), with 40 trials per session (20 hand, 20 elbow). Feature extraction used filter-bank common spatial patterns (FBCSP) with alpha (8-13 Hz) and beta (20-25 Hz) bands, classified via FBCSP+SVM, and evaluated with 5-fold within-subject cross-validation.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Xuelin Ma\": {\n      \"orcid\": \"0000-0001-7944-1981\"\n    },\n    \"Shuang Qiu\": {},\n    \"Changde Du\": {},\n    \"Junfeng Xing\": {},\n    \"Huiguang He\": {\n      \"orcid\": \"0000-0002-0684-1711\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"same limb\"\n    },\n    {\n      \"term\": \"hand\"\n    },\n    {\n      \"term\": \"elbow\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-020-0535-2\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    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He","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000167-blue)](https://doi.org/10.82901/nemar.nm000167)\n\n# Motor imagery dataset from Ma et al. 2020\n\nMotor imagery dataset from Ma et al. 2020.\n\n## Dataset Overview\n\n- **Code**: Ma2020\n- **Paradigm**: imagery\n- **DOI**: 10.1038/s41597-020-0535-2\n- **Subjects**: 25\n- **Sessions per subject**: 15\n- **Events**: right_hand=1, right_elbow=2\n- **Trial interval**: [0, 4] s\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- **Ground**: AFz\n- **Line frequency**: 50.0 Hz\n- **Impedance threshold**: 5 kOhm\n- **Auxiliary channels**: EOG (2 ch, horizontal, vertical), M2\n\n## Participants\n\n- **Number of subjects**: 25\n- **Health status**: healthy\n- **Age**: mean=25.56, min=23, max=29\n- **Gender distribution**: male=18, female=7\n- **Handedness**: {'right': 25}\n- **BCI experience**: naive\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Task type**: motor_imagery_same_limb\n- **Number of classes**: 2\n- **Class labels**: right_hand, right_elbow\n- **Trial duration**: 4.0 s\n- **Feedback type**: none\n- **Stimulus type**: visual cue\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: False\n- **Instructions**: Subjects were asked to concentrate on performing the indicated motor imagery task (right hand or right elbow) using kinesthetic, not visual, motor imagery while avoiding any motion during imagination.\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n  right_elbow\n    ├─ Sensory-event\n    └─ Label/right_elbow\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: right_hand, right_elbow\n- **Cue duration**: 1.0 s\n- **Imagery duration**: 4.0 s\n\n## Data Structure\n\n- **Trials**: 600\n- **Trials per class**: right_hand=300, right_elbow=300\n- **Blocks per session**: 15\n- **Trials context**: 3 days x 5 MI sessions/day = 15 sessions, 40 trials/session (20 hand + 20 elbow)\n\n## Signal Processing\n\n- **Classifiers**: FBCSP+SVM\n- **Feature extraction**: FBCSP\n- **Frequency bands**: alpha=[8.0, 13.0] Hz; beta=[20.0, 25.0] Hz\n- **Spatial filters**: CAR, FBCSP\n\n## Cross-Validation\n\n- **Method**: 5-fold\n- **Folds**: 5\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: motor_rehabilitation, prosthetic_control\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: healthy\n- **Modality**: motor\n- **Type**: imagery\n\n## Documentation\n\n- **DOI**: 10.1038/s41597-020-0535-2\n- **License**: CC-BY-4.0\n- **Investigators**: Xuelin Ma, Shuang Qiu, Changde Du, Junfeng Xing, Huiguang He\n- **Senior author**: Huiguang He\n- **Institution**: Chinese Academy of Sciences\n- **Department**: Institute of Automation\n- **Country**: CN\n- **Repository**: Harvard Dataverse\n- **Data URL**: https://doi.org/10.7910/DVN/RBN3XG\n- **Publication year**: 2020\n- **Funding**: National Key Research and Development Plan of China (No. 2017YFB1002502); National Natural Science Foundation of China (No. 61976209); National Natural Science Foundation of China (No. 61906188)\n- **Ethics approval**: Ethics Committee of the Institute of Automation, Chinese Academy of Sciences\n- **Keywords**: motor imagery, EEG, BCI, same limb, hand, elbow\n\n## References\n\nX. Ma, S. Qiu, C. Du, J. Xing, and H. He, \"Multi-channel EEG recording during motor imagery of different joints from the same limb,\" Scientific Data, vol. 7, no. 1, p. 191, 2020. DOI: 10.1038/s41597-020-0535-2\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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