{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd4","dataset_id":"nm000311","associated_paper_doi":"10.1093/gigascience/giaa098","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"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000311","datatypes":["eeg"],"demographics":{"subjects_count":25,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":{"r":25}},"experimental_modalities":null,"external_links":{"paper_url":"https://academic.oup.com/gigascience/article-pdf/9/10/giaa098/33842205/giaa098.pdf"},"funding":[],"ingestion_fingerprint":"f5cec11f982d50b86acc2ae94b1f1b4d1cff9e5da4c673c2cc6b858eb607771c","license":"CC0-1.0","n_contributing_labs":null,"name":"Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000311-blue)](https://doi.org/10.82901/nemar.nm000311)\nJeong2020\n=========\nMultimodal MI+ME dataset from Jeong et al 2020.\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\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}\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\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\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  reach_forward\n    ├─ Sensory-event\n    └─ Label/reach_forward\n  reach_backward\n    ├─ Sensory-event\n    └─ Label/reach_backward\n  reach_left\n    ├─ Sensory-event\n    └─ Label/reach_left\n  reach_right\n    ├─ Sensory-event\n    └─ Label/reach_right\n  reach_up\n    ├─ Sensory-event\n    └─ Label/reach_up\n  reach_down\n    ├─ Sensory-event\n    └─ Label/reach_down\n  grasp_cup\n    ├─ Sensory-event\n    └─ Label/grasp_cup\n  grasp_ball\n    ├─ Sensory-event\n    └─ Label/grasp_ball\n  grasp_card\n    ├─ Sensory-event\n    └─ Label/grasp_card\n  twist_pronation\n    ├─ Sensory-event\n    └─ Label/twist_pronation\n  twist_supination\n    ├─ Sensory-event\n    └─ Label/twist_supination\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\nData Structure\n--------------\n  Trials: 41250\n  Trials context: 25 subjects x 3 sessions x 550 trials (300 reaching + 150 grasping + 100 twisting)\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\nCross-Validation\n----------------\n  Method: 10x10-fold\n  Folds: 10\n  Evaluation type: within_session\nBCI Application\n---------------\n  Applications: motor_control, prosthetics\n  Environment: laboratory\n  Online feedback: False\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\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\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\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---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0","1","2"],"size_bytes":95161219492,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000311","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-05-31T16:36:48.716383+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":213,"computed_title":"Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)","nchans_counts":[{"val":71,"count":213}],"sfreq_counts":[{"val":1000.0,"count":213}],"stats_computed_at":"2026-05-31T19:34:32.604908+00:00","total_duration_s":446631.787,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Motor"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery Dataset is the closest match: participants perform visually cued movement and imagery, labeled Healthy, Visual, and Motor. It establishes that the cue determines modality while movement or imagery determines type.","metadata_analysis":"The readme states \"Health status: healthy\" and \"Pathology: Healthy.\" It describes \"MI and real movement conditions\" involving \"11 intuitive upper-limb movement tasks.\" Stimulus fields specify \"Stimulus type: text cues,\" \"Stimulus modalities: visual,\" and \"Primary modality: visual.\" Its \"multimodal\" description refers to EEG, EOG, and EMG recordings, not multiple sensory stimulus channels.","paper_abstract_analysis":"The abstract confirms \"25 healthy participants\" and identifies the research focus as \"real-movement and motor imagery.\" It does not indicate a clinical cohort or an additional sensory stimulus channel.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\"; the motor-imagery few-shot example suggests Healthy. ALIGN. Modality: Metadata says \"Stimulus modalities: visual\"; the example's screen cues suggest Visual. ALIGN. Type: Metadata says \"MI and real movement conditions\"; the example maps movement and imagery to Motor. ALIGN. No metadata fact is overridden.","decision_summary":"Pathology—Healthy versus Unknown: Healthy wins on \"Health status: healthy\" and \"Pathology: Healthy,\" reinforced by the abstract and few-shot match (0.8). Modality—Visual versus Multisensory: Visual wins on \"Stimulus type: text cues,\" \"Stimulus modalities: visual,\" and \"Primary modality: visual\"; multiple recording signals do not make the stimuli multisensory (0.9). Type—Motor versus Other: Motor wins on \"MI and real movement conditions,\" \"11 intuitive upper-limb movement tasks,\" and \"Imagery tasks: reach_forward, reach_backward, reach_left, reach_right, reach_up, reach_down, grasp_cup, grasp_ball, grasp_card, twist_pronation, twist_supination,\" with a clear few-shot match (0.9). All selections align with the evidence."}},"canonical_name":null,"name_confidence":0.78,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Jeong2020","bad_channels_info":null,"generated_by":[{"CodeURL":"https://github.com/NeuroTechX/moabb","Name":"moabb","Description":"Mother of All BCI Benchmarks","Version":"1.5.0"}],"references_and_links":["https://zenodo.org/records/19021436"],"source_datasets":[{"DOI":"10.1093/gigascience/giaa098","URL":"https://zenodo.org/records/19021436"},{"DOI":"doi:10.1093/gigascience/giaa098"}],"associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":5,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}