{"dataset":{"id":"373","dataset_id":"nm000338","name":"Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","description":"A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000338","concept_doi":"10.82901/nemar.nm000338","latest_version_doi":"10.82901/nemar.nm000338.v1.0.5","created_at":"2026-03-28 15:23:43","updated_at":"2026-08-18 18:21:03","zenodo_concept_id":"20526244","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy\",\n  \"description\": \"A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP) across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that while MI showed the highest illiteracy rate (53.7%), all participants could control at least one BCI paradigm. Data were acquired at 1000 Hz using 62 EEG channels with concurrent electromyography recordings.\",\n  \"methods_description\": \"EEG data were recorded from 54 healthy subjects (age 24-35 years) across two sessions using a BrainAmp amplifier with 62 Ag/AgCl electrodes at 1000 Hz sampling rate, nose-referenced with ground at AFz. Each session included three BCI paradigms: ERP speller, binary motor imagery (left/right hand), and SSVEP (four target frequencies: 5.45, 6.67, 8.57, 12 Hz). Motor imagery trials consisted of 4-second imagery periods cued by visual arrows, with 100 trials per class per session. Impedance was maintained below 10 kOhm. Auxiliary recordings included electromyography (4 channels), resting state EEG, and artifact data.\",\n  \"license\": \"GPL-3.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Min-Ho Lee\": {\n      \"orcid\": \"0000-0002-5730-1715\"\n    },\n    \"O-Yeon Kwon\": {\n      \"orcid\": \"0000-0001-5498-0540\"\n    },\n    \"Yong-Jeong Kim\": {\n      \"orcid\": \"0000-0003-3038-4087\"\n    },\n    \"Hong-Kyung Kim\": {\n      \"orcid\": \"0000-0002-1786-2729\"\n    },\n    \"Young-Eun Lee\": {\n      \"orcid\": \"0000-0003-2610-7028\"\n    },\n    \"John Williamson\": {\n      \"orcid\": \"0000-0001-7883-9816\"\n    },\n    \"Siamac Fazli\": {\n      \"orcid\": \"0000-0003-3397-0647\"\n    },\n    \"Seong-Whan Lee\": {\n      \"orcid\": \"0000-0002-6249-4996\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"steady-state visually evoked potential\"\n    },\n    {\n      \"term\": \"BCI illiteracy\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.5524/100542\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.1093/gigascience/giz002\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000338\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000338\",\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    \"130.1 GB (325 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"9aea55c96a7241eb1d3dd9e45d92ef16b1079646f9a2c52e208acd7de2e6758d\"\n}","last_activity_at":"2026-08-16 13:47:02","source":null,"source_id":null,"subject_count":54,"modalities":"eeg","age_min":null,"age_max":null,"file_size":130115888561,"total_files":1955,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee","license":"GPL-3.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000338-blue)](https://doi.org/10.82901/nemar.nm000338)\n\nLee2019-MI\n==========\n\nBMI/OpenBMI dataset for MI.\n\nDataset Overview\n----------------\n  Code: Lee2019-MI\n  Paradigm: imagery\n  DOI: 10.5524/100542\n  Subjects: 54\n  Sessions per subject: 2\n  Events: left_hand=2, right_hand=1\n  Trial interval: [0.0, 4.0] s\n  File format: MAT\n\nAcquisition\n-----------\n  Sampling rate: 1000.0 Hz\n  Number of channels: 62\n  Channel types: eeg=62, emg=4\n  Channel names: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EMG1, EMG2, EMG3, EMG4, F10, F3, F4, F7, F8, F9, FC1, FC2, FC3, FC4, FC5, FC6, FT10, FT9, FTT10h, FTT9h, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P7, P8, PO10, PO3, PO4, PO9, POz, Pz, T7, T8, TP10, TP7, TP8, TP9, TPP10h, TPP8h, TPP9h, TTP7h\n  Montage: standard_1005\n  Hardware: BrainAmp\n  Reference: nasion\n  Ground: AFz\n  Sensor type: Ag/AgCl\n  Line frequency: 60.0 Hz\n  Impedance threshold: 10.0 kOhm\n  Auxiliary channels: EMG (4 ch)\n\nParticipants\n------------\n  Number of subjects: 54\n  Health status: healthy\n  Age: min=24, max=35\n  Gender distribution: female=25, male=29\n  Handedness: {'right': 50, 'left': 2, 'ambidexter': 2}\n  BCI experience: mixed\n\nExperimental Protocol\n---------------------\n  Paradigm: imagery\n  Number of classes: 2\n  Class labels: left_hand, right_hand\n  Trial duration: 4.0 s\n  Tasks: MI\n  Study design: Binary-class motor imagery (left/right hand grasping). Two sessions on different days, each with offline training and online test phases of 100 trials each.\n  Feedback type: visual\n  Stimulus type: arrow\n  Stimulus modalities: visual\n  Primary modality: visual\n  Synchronicity: synchronous\n  Mode: both\n  Training/test split: True\n  Instructions: Subjects performed the imagery task of grasping with the appropriate hand for 4 s when the right or left arrow appeared as a visual cue. First 3 s of each trial began with a black fixation cross to prepare subjects for the MI task. After each task, the screen remained blank for 6 s (± 1.5 s).\n\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n  left_hand\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Leftward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n  right_hand\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Rightward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: motor_imagery\n  Imagery tasks: left_hand, right_hand\n  Cue duration: 3.0 s\n  Imagery duration: 4.0 s\n\nData Structure\n--------------\n  Trials: 200\n  Trials per class: left_hand=100, right_hand=100\n  Trials context: 100 trials per session per phase (50 per class per phase). Training: 50 left + 50 right. Test: 50 left + 50 right. Total per session: 200.\n\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\n\nSignal Processing\n-----------------\n  Classifiers: CSP+LDA, CSSP, FBCSP, BSSFO\n  Feature extraction: CSP, CSSP, FBCSP, BSSFO, log-variance\n  Frequency bands: mu=[8.0, 12.0] Hz; analyzed=[8.0, 30.0] Hz\n  Spatial filters: CSP, CSSP, FBCSP, BSSFO\n\nCross-Validation\n----------------\n  Method: train-test split\n  Evaluation type: within_session, cross_session\n\nPerformance (Original Study)\n----------------------------\n  Accuracy: 71.1%\n  Accuracy Std: 0.15\n  Illiteracy Rate: 53.7\n  Session1 Accuracy: 70.0\n  Session2 Accuracy: 72.2\n\nBCI Application\n---------------\n  Applications: motor_control\n  Environment: laboratory\n  Online feedback: True\n\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\n\nDocumentation\n-------------\n  Description: EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Includes MI, ERP, and SSVEP paradigms with a large number of subjects over multiple sessions.\n  DOI: 10.1093/gigascience/giz002\n  License: GPL-3.0\n  Investigators: Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee\n  Senior author: Seong-Whan Lee\n  Contact: sw.lee@korea.ac.kr\n  Institution: Korea University\n  Department: Department of Brain and Cognitive Engineering\n  Address: 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea\n  Country: KR\n  Repository: GigaDB\n  Publication year: 2019\n  How to acknowledge: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.\n  Keywords: EEG datasets, brain-computer interface, event-related potential, steady-state visually evoked potential, motor-imagery, OpenBMI toolbox, BCI illiteracy\n\nAbstract\n--------\nElectroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system.\n\nMethodology\n-----------\nExperimental procedure: 54 healthy subjects participated in two sessions on different days. Each session consisted of three BCI paradigms performed sequentially: ERP speller (36 symbols, row-column presentation with face stimuli), MI task (binary left/right hand imagery), and SSVEP (four target frequencies: 5.45, 6.67, 8.57, 12 Hz). Each paradigm had offline training and online test phases. EEG recorded at 1000 Hz with 62 Ag/AgCl electrodes using BrainAmp amplifier, nose-referenced, grounded to AFz. Impedance maintained below 10 kOhm. Subjects seated 60 cm from 21-inch LCD monitor. Questionnaires collected demographic, physiological, and psychological data. Artifact data (eye blinking, eye movements, teeth clenching, arm flexing) and resting state EEG also recorded. Total experiment duration: ~205 minutes per session.\n\nReferences\n----------\nLee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E., Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience, 8(5), 1–16. https://doi.org/10.1093/gigascience/giz002\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). 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