{"dataset":{"id":"344","dataset_id":"nm000311","name":"Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)","description":"A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000311","concept_doi":"10.82901/nemar.nm000311","latest_version_doi":"10.82901/nemar.nm000311.v1.0.2","created_at":"2026-03-28 02:34:25","updated_at":"2026-08-18 18:21:44","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)\",\n  \"description\": \"A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).\",\n  \"methods_description\": \"EEG data were acquired using a 71-channel BrainAmp system (BrainProducts GmbH) with actiCap sensors at 1000 Hz sampling rate. Recordings included 60 EEG channels referenced to FCz with ground at Fpz, 4 EOG channels, and 7 EMG channels. Online filters applied: highpass 0.016 Hz, lowpass 1000 Hz. Participants performed 11 motor imagery tasks cued by visual text stimuli in a synchronous, offline paradigm across 3 sessions with 3 runs per session.\",\n  \"license\": \"CC0-1.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Ji-Hoon Jeong\": {\n      \"orcid\": \"0000-0001-6940-2700\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea\"\n        }\n      ]\n    },\n    \"Jeong-Hyun Cho\": {},\n    \"Kyung-Hwan Shim\": {},\n    \"Byoung-Hee Kwon\": {},\n    \"Byeong-Hoo Lee\": {},\n    \"Do-Yeun Lee\": {},\n    \"Dae-Hyeok Lee\": {},\n    \"Seong-Whan Lee\": {\n      \"orcid\": \"0000-0002-6249-4996\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea\"\n        },\n        {\n          \"name\": \"Department of Artificial Intelligence, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea\"\n        }\n      ]\n    }\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\": \"upper extremity\"\n    },\n    {\n      \"term\": \"electromyography\"\n    },\n    {\n      \"term\": \"prosthetics\"\n    },\n    {\n      \"term\": \"reaching\"\n    },\n    {\n      \"term\": \"grasping\"\n    },\n    {\n      \"term\": \"wrist twisting\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1093/gigascience/giaa098\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000311\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000311\",\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    \"346.6 GB (1063 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".eeg\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"23af9bf15fafa517faaf2dc6a5ae6e3e4582f94f40ad9944f2d498fb0a5080dc\"\n}","last_activity_at":"2026-08-16 14:01:22","source":null,"source_id":null,"subject_count":25,"modalities":"eeg","age_min":null,"age_max":null,"file_size":349748825198,"total_files":2717,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee","license":"CC0-1.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000311-blue)](https://doi.org/10.82901/nemar.nm000311)\n\nJeong2020\n=========\n\nMultimodal MI+ME dataset from Jeong et al 2020.\n\nDataset Overview\n----------------\n  Code: Jeong2020\n  Paradigm: imagery\n  DOI: 10.1093/gigascience/giaa098\n  Subjects: 25\n  Sessions per subject: 3\n  Events: reach_forward=1, reach_backward=2, reach_left=3, reach_right=4, reach_up=5, reach_down=6, grasp_cup=7, grasp_ball=8, grasp_card=9, twist_pronation=10, twist_supination=11\n  Trial interval: [0, 4] s\n  Runs per session: 3\n  File format: BrainVision\n\nAcquisition\n-----------\n  Sampling rate: 1000.0 Hz\n  Number of channels: 71\n  Channel types: eeg=60, eog=4, emg=7\n  Channel names: Fp1, AF7, AF3, AFz, F7, F5, F3, F1, Fz, FT7, FC5, FC3, FC1, T7, C5, C3, C1, Cz, TP7, CP5, CP3, CP1, CPz, P7, P5, P3, P1, Pz, PO7, PO3, POz, Fp2, AF4, AF8, F2, F4, F6, F8, FC2, FC4, FC6, FT8, C2, C4, C6, T8, CP2, CP4, CP6, TP8, P2, P4, P6, P8, PO4, PO8, O1, Oz, O2, Iz\n  Montage: standard_1005\n  Hardware: BrainAmp (BrainProducts GmbH)\n  Reference: FCz\n  Ground: Fpz\n  Sensor type: actiCap\n  Line frequency: 60.0 Hz\n  Online filters: {'highpass': 0.016, 'lowpass': 1000}\n\nParticipants\n------------\n  Number of subjects: 25\n  Health status: healthy\n  Age: min=24.0, max=32.0\n  Gender distribution: female=10, male=15\n  Handedness: right-handed\n  BCI experience: naive\n  Species: human\n\nExperimental Protocol\n---------------------\n  Paradigm: imagery\n  Number of classes: 11\n  Class labels: reach_forward, reach_backward, reach_left, reach_right, reach_up, reach_down, grasp_cup, grasp_ball, grasp_card, twist_pronation, twist_supination\n  Trial duration: 4.0 s\n  Study design: 11 intuitive upper-limb movement tasks: 6 reaching + 3 grasping + 2 wrist twisting. MI and real movement conditions, 3 sessions.\n  Feedback type: none\n  Stimulus type: text cues\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  reach_forward\n    ├─ Sensory-event\n    └─ Label/reach_forward\n\n  reach_backward\n    ├─ Sensory-event\n    └─ Label/reach_backward\n\n  reach_left\n    ├─ Sensory-event\n    └─ Label/reach_left\n\n  reach_right\n    ├─ Sensory-event\n    └─ Label/reach_right\n\n  reach_up\n    ├─ Sensory-event\n    └─ Label/reach_up\n\n  reach_down\n    ├─ Sensory-event\n    └─ Label/reach_down\n\n  grasp_cup\n    ├─ Sensory-event\n    └─ Label/grasp_cup\n\n  grasp_ball\n    ├─ Sensory-event\n    └─ Label/grasp_ball\n\n  grasp_card\n    ├─ Sensory-event\n    └─ Label/grasp_card\n\n  twist_pronation\n    ├─ Sensory-event\n    └─ Label/twist_pronation\n\n  twist_supination\n    ├─ Sensory-event\n    └─ Label/twist_supination\n\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: motor_imagery\n  Imagery tasks: reach_forward, reach_backward, reach_left, reach_right, reach_up, reach_down, grasp_cup, grasp_ball, grasp_card, twist_pronation, twist_supination\n  Imagery duration: 4.0 s\n\nData Structure\n--------------\n  Trials: 41250\n  Trials context: 25 subjects x 3 sessions x 550 trials (300 reaching + 150 grasping + 100 twisting)\n\nSignal Processing\n-----------------\n  Classifiers: CSP+RLDA\n  Feature extraction: CSP\n  Frequency bands: mu_beta=[8.0, 30.0] Hz\n  Spatial filters: CSP\n\nCross-Validation\n----------------\n  Method: 10x10-fold\n  Folds: 10\n  Evaluation type: within_session\n\nBCI Application\n---------------\n  Applications: motor_control, prosthetics\n  Environment: laboratory\n  Online feedback: False\n\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\n\nDocumentation\n-------------\n  DOI: 10.1093/gigascience/giaa098\n  License: CC0-1.0\n  Investigators: Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee\n  Institution: Korea University\n  Country: KR\n  Data URL: https://zenodo.org/records/19021436\n  Publication year: 2020\n\nReferences\n----------\nJeong, J.-H., Cho, J.-H., Shim, K.-H., et al. (2020). Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions. GigaScience, 9(10), giaa098. https://doi.org/10.1093/gigascience/giaa098\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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb\n","bids_version":"1.9.0","sessions_count":3,"publish_date":"2026-04-03 14:36:29","embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-04 08:27:12","zarr_store_count":213,"zarr_index_etag":"05f031f82fd7931f05fb0e30145e5e2b","zarr_source_commit":"aa231e9a7b1c1df487ff3fcd0c13767fcf0bbe4c","archive_status":null,"archive_size":76156273246,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 325.7 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":82,"n_channels":71,"electrode_system":"10-10","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":346593573904,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":446632,"recording_duration_min":1018,"recording_duration_max":4850,"recording_count":213,"recordings_unavailable":0,"recordings_measured":213,"channel_count_min":71,"channel_count_max":71,"sampling_frequency":1000,"power_line_frequency":60,"eeg_reference":"FCz","placement_scheme":"10-05 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:20:57\",\"metadata_updated_at\":\"2026-08-18 18:21:42\",\"archive_checked_at\":\"2026-08-18 18:24:25\",\"zarr_checked_at\":\"2026-06-07 17:58:38\",\"records_checked_at\":\"2026-08-18 18:25:00\",\"citations_updated_at\":\"2026-09-06 03:00:27\",\"channel_montage_checked_at\":\"2026-06-28 23:03:37\",\"hed_checked_at\":\"2026-06-30 04:33:57\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:10:42\",\"signal_defaults_at\":\"2026-09-02 11:52:08\",\"recording_stats_at\":\"2026-09-05 03:02:03\"}","participants":25,"num_citations":82,"latest_version":"v1.0.2","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"326 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000311/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}