{"dataset":{"id":"184","dataset_id":"nm000151","name":"Motor imagery dataset for three imaginary states of the same upper extremity","description":"This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000151","concept_doi":"10.82901/nemar.nm000151","latest_version_doi":"10.82901/nemar.nm000151.v1.0.3","created_at":"2026-03-22 19:17:29","updated_at":"2026-08-20 19:21:27","zenodo_concept_id":"19631865","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Motor imagery dataset for three imaginary states of the same upper extremity\",\n  \"description\": \"This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.\",\n  \"methods_description\": \"EEG data were recorded at a 1000 Hz sampling rate using a 32-channel EGI Geodesic Net Amps 400 system with GSN-HydroCel-32 montage, referenced to Cz, with online bandpass filtering (0.1-100 Hz) and a 60 Hz line frequency. Each of the 12 subjects completed 4 sessions, with 20 trials per class (rest, imagined grasping, imagined elbow flexion) per session, using visual cues in a synchronous, offline paradigm without feedback.\",\n  \"license\": \"CC0-1.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Mojgan Tavakolan\": {},\n    \"Zack Frehlick\": {},\n    \"Xinyi Yong\": {},\n    \"Carlo Menon\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"BCI\"\n    },\n    {\n      \"term\": \"Upper Extremity\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D034941\"\n    },\n    {\n      \"term\": \"support vector machine\"\n    },\n    {\n      \"term\": \"Rehabilitation\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D012046\"\n    },\n    {\n      \"term\": \"time-domain features\"\n    },\n    {\n      \"term\": \"same limb\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1371/journal.pone.0174161\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000151\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.5061/dryad.6qs86\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\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/nm000151\",\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    \"55.8 GB (157 files)\"\n  ],\n  \"formats\": [\n    \".DAT\",\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\",\n    \".zip\"\n  ],\n  \"source_hash\": \"300f4ca760ed22176083d0f1208b9826e08927c32e1c7c64317854ffb4144ffa\"\n}","last_activity_at":"2026-08-16 13:25:22","source":null,"source_id":null,"subject_count":12,"modalities":"eeg","age_min":null,"age_max":null,"file_size":55806132823,"total_files":627,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon","license":"CC0-1.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000151-blue)](https://doi.org/10.82901/nemar.nm000151)\n\n# Motor imagery dataset for three imaginary states of the same upper extremity\n\nMotor imagery dataset for three imaginary states of the same upper extremity.\n\n## Dataset Overview\n\n- **Code**: Tavakolan2017\n- **Paradigm**: imagery\n- **DOI**: 10.1371/journal.pone.0174161\n- **Subjects**: 12\n- **Sessions per subject**: 4\n- **Events**: rest=1, right_hand=2, right_elbow_flexion=3\n- **Trial interval**: [0, 3] s\n- **File format**: BCI2000\n\n## Acquisition\n\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 32\n- **Channel types**: eeg=32\n- **Montage**: GSN-HydroCel-32\n- **Hardware**: EGI Geodesic Net Amps 400 series\n- **Reference**: Cz\n- **Sensor type**: Ag/AgCl sponge\n- **Line frequency**: 60.0 Hz\n- **Online filters**: {'bandpass': [0.1, 100]}\n- **Impedance threshold**: 50 kOhm\n\n## Participants\n\n- **Number of subjects**: 12\n- **Health status**: healthy\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 3\n- **Class labels**: rest, right_hand, right_elbow_flexion\n- **Trial duration**: 3.0 s\n- **Study design**: Three-class motor imagery of the same upper extremity: rest, grasping (MI-GRASP), and elbow flexion (MI-ELBOW). 20 trials per class per session, 4 sessions per subject.\n- **Feedback type**: none\n- **Stimulus type**: visual cue\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Instructions**: REST: relax without movement. MI-GRASP: imagine opening and closing all fingers to grab an object. MI-ELBOW: imagine moving the forearm up and down.\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\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_flexion\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Flex\n          └─ Right, Elbow\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: rest, right_hand, right_elbow_flexion\n- **Cue duration**: 3.0 s\n- **Imagery duration**: 3.0 s\n\n## Data Structure\n\n- **Trials**: 2880\n- **Trials per class**: rest=20, right_hand=20, right_elbow_flexion=20\n- **Trials context**: 12 subjects x 4 sessions x 60 trials (20 per class)\n\n## Preprocessing\n\n- **Data state**: continuous\n\n## Signal Processing\n\n- **Classifiers**: SVM-RBF\n- **Feature extraction**: autoregressive_coefficients, waveform_length, root_mean_square\n- **Frequency bands**: bandpass=[6.0, 35.0] Hz\n\n## Cross-Validation\n\n- **Method**: 10x10-fold\n- **Folds**: 10\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: motor_control, rehabilitation\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**: 10.1371/journal.pone.0174161\n- **License**: CC0-1.0\n- **Investigators**: Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon\n- **Senior author**: Carlo Menon\n- **Institution**: Simon Fraser University\n- **Department**: MENRVA Research Group, Schools of Mechatronic Systems Engineering and Engineering Science\n- **Country**: CA\n- **Repository**: Zenodo\n- **Data URL**: https://zenodo.org/records/18967205\n- **Publication year**: 2017\n- **Ethics approval**: Simon Fraser University Office of Research Ethics\n- **Keywords**: motor imagery, EEG, upper extremity, same limb, time-domain features, SVM, BCI\n\n## References\n\nM. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, \"Classifying three imaginary states of the same upper extremity using time-domain features,\" PLoS ONE, vol. 12, no. 3, e0174161, 2017. DOI: 10.1371/journal.pone.0174161\n\nM. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, \"Data from: Classifying three imaginary states of the same upper extremity using time-domain features,\" Dryad, 2017. DOI: 10.5061/dryad.6qs86\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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