{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cdd","dataset_id":"nm000339","associated_paper_doi":"10.1038/s41597-021-00883-1","authors":["James R. Stieger","Stephen A. Engel","Bin He"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1038/s41597-021-00883-1","datatypes":["eeg"],"demographics":{"subjects_count":62,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":{"r":62}},"experimental_modalities":null,"external_links":{"paper_url":"https://www.nature.com/articles/s41597-021-00883-1.pdf"},"funding":["NIH AT009263","NIH EB021027","NIH NS096761","NIH MH114233","NIH EB029354"],"ingestion_fingerprint":"697525377e132d5a2ddf9a62cf6fae7aec88c9d34d75e72197750cdba1dbb4f9","license":"CC-BY-NC-4.0","n_contributing_labs":null,"name":"Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population","readme":"Stieger2021\n===========\nMotor Imagery dataset from Stieger et al. 2021 [1]_.\nDataset Overview\n----------------\n  Code: Stieger2021\n  Paradigm: imagery\n  DOI: 10.1038/s41597-021-00883-1\n  Subjects: 62\n  Sessions per subject: 11\n  Events: right_hand=1, left_hand=2, both_hand=3, rest=4\n  Trial interval: [0, 3] s\n  File format: MAT\nAcquisition\n-----------\n  Sampling rate: 1000.0 Hz\n  Number of channels: 62\n  Channel types: eeg=62\n  Channel names: AF3, AF4, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO5, PO6, PO7, PO8, POz, Pz, T7, T8, TP7, TP8\n  Montage: 10-10\n  Hardware: Neuroscan SynAmps RT amplifiers\n  Software: Neuroscan\n  Sensor type: EEG\n  Line frequency: 60.0 Hz\n  Online filters: 0.1 to 200 Hz with 60 Hz notch filter\n  Impedance threshold: 5.0 kOhm\n  Cap manufacturer: Neuroscan\n  Cap model: Quik-Cap\nParticipants\n------------\n  Number of subjects: 62\n  Health status: healthy\n  Age: min=18, max=63\n  Gender distribution: male=13, female=49\n  Handedness: mostly right-handed\n  Species: human\nExperimental Protocol\n---------------------\n  Paradigm: imagery\n  Number of classes: 4\n  Class labels: right_hand, left_hand, both_hand, rest\n  Tasks: LR, UD, 2D\n  Study design: longitudinal training study with intervention\n  Feedback type: visual\n  Stimulus type: target_bar\n  Stimulus modalities: visual\n  Primary modality: visual\n  Mode: online\n  Instructions: Imagine your left (right) hand opening and closing to move the cursor left (right). Imagine both hands opening and closing to move the cursor up. Finally, to move the cursor down, voluntarily rest; in other words, clear your mind.\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n  both_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine, Move, Hand\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: motor_imagery\n  Imagery tasks: left_hand, right_hand, both_hands, rest\n  Cue duration: 2.0 s\n  Imagery duration: 6.0 s\nData Structure\n--------------\n  Trials: 450\n  Blocks per session: 18\n  Trials context: per_session\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\nSignal Processing\n-----------------\n  Feature extraction: ERD, ERS, autoregressive model, power spectrum\n  Frequency bands: alpha=[10.5, 13.5] Hz; mu=[8, 14] Hz\n  Spatial filters: Laplacian (C3/C4 with 4 surrounding electrodes)\nCross-Validation\n----------------\n  Evaluation type: cross_session\nPerformance (Original Study)\n----------------------------\n  Accuracy: 70.0%\n  Pvc 1D Threshold: 70.0\n  Pvc 2D Threshold: 40.0\nBCI Application\n---------------\n  Applications: cursor_control\n  Environment: laboratory\n  Online feedback: True\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Active\nDocumentation\n-------------\n  Description: Continuous sensorimotor rhythm based brain computer interface learning in a large population\n  DOI: 10.1038/s41597-021-00883-1\n  License: CC-BY-NC-4.0\n  Investigators: James R. Stieger, Stephen A. Engel, Bin He\n  Senior author: Bin He\n  Contact: bhe1@andrew.cmu.edu\n  Institution: Carnegie Mellon University, University of Minnesota\n  Department: Carnegie Mellon University, Pittsburgh, PA, USA; University of Minnesota, Minneapolis, MN, USA\n  Address: Pittsburgh, PA, USA; Minneapolis, MN, USA\n  Country: US\n  Repository: GitHub\n  Data URL: https://doi.org/10.6084/m9.figshare.13123148.v1\n  Publication year: 2021\n  Funding: NIH AT009263; NIH EB021027; NIH NS096761; NIH MH114233; NIH EB029354\n  Ethics approval: University of Minnesota IRB; Carnegie Mellon University IRB\n  Keywords: BCI, sensorimotor rhythm, motor imagery, EEG, longitudinal, learning\nAbstract\n--------\nBrain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheelchairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks.\nMethodology\n-----------\nParticipants completed 7-11 online BCI training sessions. Each session consisted of 450 trials across 3 tasks (LR, UD, 2D) with 6 runs total. Each trial: 2s inter-trial interval, 2s target presentation, up to 6s feedback control. Online control used spatial filtering (Laplacian around C3/C4), autoregressive model (order 16) for spectrum estimation, alpha power (12 Hz ± 1.5 Hz) for control signal. Horizontal motion controlled by lateralized alpha power (C4-C3), vertical motion by total alpha power (C4+C3). Control signals normalized to zero mean and unit variance. Cursor position updated every 40 ms.\nReferences\n----------\nStieger, J. R., Engel, S. A., & He, B. (2021). Continuous sensorimotor rhythm based brain computer interface learning in a large population. Scientific Data, 8(1), 98. https://doi.org/10.1038/s41597-021-00883-1\nNotes\n.. versionadded:: 1.1.0\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\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":["1","10","11","2","3","4","5","6","7","8","9"],"size_bytes":398846611562,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000339","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-04-30T14:09:55.196759+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:12:09Z"},"total_files":598,"computed_title":"Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population","nchans_counts":[{"val":60,"count":598}],"sfreq_counts":[{"val":1000.0,"count":598}],"stats_computed_at":"2026-05-01T13:49:34.659737+00:00","total_duration_s":2215269.402,"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","Learning"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery Dataset is the closest paradigm match: visually presented targets cue hand imagery, mapped to Healthy, Visual, and Motor. It supports labeling this dataset's motor-imagery BCI by its visual cues and research focus, rather than treating the imagined movement as a stimulus modality.","metadata_analysis":"The readme states \"Health status: healthy\" and describes \"62 healthy adults.\" It specifies \"Stimulus modalities: visual,\" \"Stimulus type: target_bar,\" and a \"Cue duration: 2.0 s.\" Its focus is a \"Motor Imagery dataset\" with \"4 different motor-imagery-based BCI tasks\"; it also calls the design a \"longitudinal training study\" examining \"how individuals learn to control SMR-BCIs.\" The readme tag \"Modality: Motor\" describes the task domain, not the sensory channel, and is not an allowed modality label.","paper_abstract_analysis":"The abstract confirms recruitment of \"62 healthy adults\" and says the study examined \"how individuals learn to control SMR-BCIs\" through \"motor-imagery-based BCI tasks.\" It reinforces Motor and a distinct Learning aim without indicating a clinical cohort or therapy.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\"; the healthy motor-imagery few-shot suggests Healthy. ALIGN. Modality: Metadata says \"Stimulus modalities: visual\"; the motor-imagery few-shot suggests Visual. ALIGN. The readme's \"Modality: Motor\" tag conflicts with the sensory-channel definition; the explicit visual-stimulus facts win. Type: Metadata says \"Motor Imagery dataset\" and \"how individuals learn to control SMR-BCIs\"; the few-shot suggests Motor. ALIGN for Motor, with metadata additionally supporting Learning. No metadata clinical-population fact is overridden.","decision_summary":"Pathology—Healthy versus Unknown: \"Health status: healthy,\" \"62 healthy adults,\" and the abstract's \"62 healthy adults\" strongly favor Healthy; confidence 0.9. Modality—Visual versus No stimulus: \"Stimulus modalities: visual,\" \"Stimulus type: target_bar,\" and \"Cue duration: 2.0 s\" establish visual cues; confidence 0.9. Type—Motor versus Learning: \"Motor Imagery dataset\" and \"4 different motor-imagery-based BCI tasks\" make Motor the stronger paradigm label, while \"longitudinal training study\" and \"how individuals learn to control SMR-BCIs\" establish Learning as a genuine second aim. Assign both, with confidence 0.9 given the explicit evidence and close motor-imagery few-shot match. Training alone does not establish a therapeutic Intervention."}},"canonical_name":null,"name_confidence":0.82,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Stieger2021","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}