{"dataset":{"id":"374","dataset_id":"nm000339","name":"Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population","description":"A large-scale longitudinal dataset of sensorimotor rhythm-based brain-computer interface (BCI) training in 62 healthy adults. The dataset comprises over 600 hours of EEG recordings across 598 sessions with more than 250,000 trials of motor imagery tasks (left hand, right hand, both hands, and rest). This resource enables investigation of BCI learning dynamics and algorithm development for non-invasive neural control applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000339","concept_doi":"10.82901/nemar.nm000339","latest_version_doi":"10.82901/nemar.nm000339.v1.0.4","created_at":"2026-03-28 15:23:48","updated_at":"2026-08-18 18:19:30","zenodo_concept_id":"20526488","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population\",\n  \"description\": \"A large-scale longitudinal dataset of sensorimotor rhythm-based brain-computer interface (BCI) training in 62 healthy adults. The dataset comprises over 600 hours of EEG recordings across 598 sessions with more than 250,000 trials of motor imagery tasks (left hand, right hand, both hands, and rest). This resource enables investigation of BCI learning dynamics and algorithm development for non-invasive neural control applications.\",\n  \"methods_description\": \"Participants completed 7-11 online BCI training sessions. Each session consisted of 450 trials across 3 tasks (left-right, up-down, 2D) with 6 runs. EEG was recorded from 62 channels using Neuroscan SynAmps RT amplifiers at 1000 Hz sampling rate with 10-10 montage. Online control employed spatial filtering (Laplacian around C3/C4), autoregressive spectral estimation (order 16), and alpha power (12 Hz ± 1.5 Hz) for generating control signals. Horizontal cursor motion was controlled by lateralized alpha power (C4-C3), vertical motion by total alpha power (C4+C3).\",\n  \"license\": \"CC-BY-NC-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"James R. Stieger\": {},\n    \"Stephen A. Engel\": {},\n    \"Bin He\": {\n      \"orcid\": \"0000-0003-2944-8602\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"sensorimotor rhythm\"\n    },\n    {\n      \"term\": \"learning\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"BCI\"\n    },\n    {\n      \"term\": \"longitudinal\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-021-00883-1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.6084/m9.figshare.13123148.v1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000339\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\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/nm000339\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"AT009263\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"EB021027\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"NS096761\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"MH114233\"\n    },\n    {\n      \"funder_name\": \"NIH\",\n      \"award_number\": \"EB029354\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"775.7 GB (1197 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"e2f5c86a4cbf3df02d71a4366df4f82a162d58ba51f7b4c67fef335c0878a6cc\"\n}","last_activity_at":"2026-08-16 13:49:40","source":null,"source_id":null,"subject_count":62,"modalities":"eeg","age_min":null,"age_max":null,"file_size":775737488950,"total_files":7187,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"James R. Stieger, Stephen A. Engel, Bin He","license":"CC-BY-NC-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000339-blue)](https://doi.org/10.82901/nemar.nm000339)\n\nStieger2021\n===========\n\nMotor Imagery dataset from Stieger et al. 2021 [1]_.\n\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\n\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\n\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\n\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.\n\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n  right_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n  left_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n  both_hand\n    ├─ Sensory-event, Experimental-stimulus, Visual-presentation\n    └─ Agent-action\n       └─ Imagine, Move, Hand\n\n  rest\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Rest\n\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\n\nData Structure\n--------------\n  Trials: 450\n  Blocks per session: 18\n  Trials context: per_session\n\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\n\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)\n\nCross-Validation\n----------------\n  Evaluation type: cross_session\n\nPerformance (Original Study)\n----------------------------\n  Accuracy: 70.0%\n  Pvc 1D Threshold: 70.0\n  Pvc 2D Threshold: 40.0\n\nBCI Application\n---------------\n  Applications: cursor_control\n  Environment: laboratory\n  Online feedback: True\n\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Active\n\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\n\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.\n\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.\n\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\n\nNotes\n\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\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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