{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cdc","dataset_id":"nm000338","associated_paper_doi":null,"authors":["Min-Ho Lee","O-Yeon Kwon","Yong-Jeong Kim","Hong-Kyung Kim","Young-Eun Lee","John Williamson","Siamac Fazli","Seong-Whan Lee"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000338","datatypes":["eeg"],"demographics":{"subjects_count":54,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000338","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"cdbfb41ac3c147bc02723959307b0efc394959ca06adff6f6a5f5fd5ceab982e","license":"GPL-3.0","n_contributing_labs":null,"name":"Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000338-blue)](https://doi.org/10.82901/nemar.nm000338)\nLee2019-MI\n==========\nBMI/OpenBMI dataset for MI.\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\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)\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\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).\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\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  right_hand\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Rightward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\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\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.\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\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\nCross-Validation\n----------------\n  Method: train-test split\n  Evaluation type: within_session, cross_session\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\nBCI Application\n---------------\n  Applications: motor_control\n  Environment: laboratory\n  Online feedback: True\nTags\n----\n  Pathology: Healthy\n  Modality: Motor\n  Type: Research\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\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.\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.\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\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":"Seong-Whan Lee","sessions":["1","2"],"size_bytes":130115888561,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000338","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-08-26T11:04:48.001099+00:00","dataset_created_at":null,"dataset_modified_at":"2026-08-18 18:18:53"},"total_files":216,"computed_title":"Lee et al. 2019 (Motor Imagery) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","nchans_counts":[{"val":66,"count":216}],"sfreq_counts":[{"val":1000.0,"count":216}],"stats_computed_at":"2026-10-15T11:39:16.407600+00:00","total_duration_s":329549.784,"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":0.9,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"The EEG Motor Movement/Imagery example is the closest match: visually cued hand imagery in healthy volunteers is labeled Healthy, Visual, Motor. Its convention applies to this motor-imagery subset, rather than to every paradigm mentioned in the broader publication.","metadata_analysis":"The readme states \"Health status: healthy\" and \"54 healthy subjects participated.\" It identifies \"Tasks: MI\" and \"Binary-class motor imagery (left/right hand grasping).\" It specifies \"Stimulus modalities: visual\" and says imagery began \"when the right or left arrow appeared as a visual cue.\" The dataset task is \"imagery,\" although the documentation also describes ERP and SSVEP data in the broader study.","paper_abstract_analysis":"The abstract embedded in the readme confirms that the broader study included MI, ERP, and SSVEP, while reporting MI results separately. The methodology calls MI a \"binary left/right hand imagery\" task. This supports classifying the supplied MI dataset, not combining all three paradigms.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\"; the healthy motor-imagery few-shot suggests Healthy. ALIGN. Modality: Metadata says \"Stimulus modalities: visual\" and describes an arrow \"as a visual cue\"; that few-shot suggests Visual for cued imagery. ALIGN. Type: Metadata says \"Binary-class motor imagery (left/right hand grasping)\"; the few-shot suggests Motor. ALIGN. No metadata fact is overridden.","decision_summary":"Pathology: Healthy versus Unknown; Healthy wins on \"Health status: healthy,\" \"54 healthy subjects participated,\" and \"Pathology: Healthy,\" plus the close few-shot match (0.9). Modality: Visual versus No stimulus; Visual wins because \"Stimulus modalities: visual,\" \"Stimulus type: arrow,\" and \"visual cue\" establish an external cue, unlike uncued imagery (0.9). Type: Motor versus Perception; Motor wins because \"Tasks: MI,\" \"Binary-class motor imagery (left/right hand grasping),\" and \"Imagery tasks: left_hand, right_hand\" make movement imagery the focus, not visual discrimination (0.9). All selections align with the few-shot convention."}},"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":"canonical","author_year":"Lee2019_MI","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":5,"is_oa":null,"oa_status":"http_404","source":"paper_resolver"},"generated_by":[{"CodeURL":"https://github.com/NeuroTechX/moabb","Name":"moabb","Description":"Mother of All BCI Benchmarks","Version":"1.5.0"}],"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.","source_datasets":[{"DOI":"10.5524/100542"},{"DOI":"doi:10.1093/gigascience/giz002"}],"bad_channels_info":null,"n_records":216}}