{"dataset":{"id":"356","dataset_id":"nm000323","name":"Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","description":"This dataset comprises EEG recordings from 54 healthy participants performing a P300-based brain-computer interface speller task, designed to investigate BCI illiteracy. The study includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with participants completing offline training and online test phases using a 36-symbol row-column speller paradigm. The dataset achieved 96.7% accuracy with an 11.1% BCI illiteracy rate, providing a comprehensive resource for evaluating P300-based BCI performance and individual differences in BCI competence.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000323","concept_doi":"10.82901/nemar.nm000323","latest_version_doi":"10.82901/nemar.nm000323.v1.0.4","created_at":"2026-03-28 05:59:47","updated_at":"2026-08-18 18:20:55","zenodo_concept_id":"20525466","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy\",\n  \"description\": \"This dataset comprises EEG recordings from 54 healthy participants performing a P300-based brain-computer interface speller task, designed to investigate BCI illiteracy. The study includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with participants completing offline training and online test phases using a 36-symbol row-column speller paradigm. The dataset achieved 96.7% accuracy with an 11.1% BCI illiteracy rate, providing a comprehensive resource for evaluating P300-based BCI performance and individual differences in BCI competence.\",\n  \"methods_description\": \"EEG data were acquired using a 62-channel BrainAmp system with Ag/AgCl electrodes at 1000 Hz sampling rate, referenced to nasion with ground at AFz. Participants performed a copy-spelling task using a 36-symbol row-column speller with visual feedback. Training involved spelling 'NEURAL NETWORKS AND DEEP LEARNING' (33 characters) and testing involved 'PATTERN RECOGNITION MACHINE LEARNING' (36 characters), with each character receiving 5 sequences of 12 flashes. Inter-stimulus interval was 135 ms with stimulus onset asynchrony of 215 ms.\",\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\": \"P300\"\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\": \"BCI illiteracy\"\n    },\n    {\n      \"term\": \"speller paradigm\"\n    },\n    {\n      \"term\": \"row-column paradigm\"\n    },\n    {\n      \"term\": \"communication\"\n    },\n    {\n      \"term\": \"steady-state visually evoked potential\"\n    },\n    {\n      \"term\": \"motor-imagery\"\n    },\n    {\n      \"term\": \"OpenBMI toolbox\"\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/nm000323\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000323\",\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    \"141.4 GB (325 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"01812b3fe4a3ebfe8fde857908e9d2aaf1c569e01522d69300d89591c72609e0\"\n}","last_activity_at":"2026-08-16 13:48:08","source":null,"source_id":null,"subject_count":54,"modalities":"eeg","age_min":29.5,"age_max":29.5,"file_size":141388340508,"total_files":1955,"tasks":"p300","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.nm000323-blue)](https://doi.org/10.82901/nemar.nm000323)\n\nLee2019-ERP\n===========\n\nBMI/OpenBMI dataset for P300.\n\nDataset Overview\n----------------\n  Code: Lee2019-ERP\n  Paradigm: p300\n  DOI: 10.5524/100542\n  Subjects: 54\n  Sessions per subject: 2\n  Events: Target=1, NonTarget=2\n  Trial interval: [0.0, 1.0] s\n  Runs per session: 2\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  Software: OpenBMI\n  Reference: nasion\n  Ground: AFz\n  Sensor type: Ag/AgCl\n  Line frequency: 60.0 Hz\n  Impedance threshold: 10 kOhm\n  Cap manufacturer: Brain Products\n  Auxiliary channels: EMG (4 ch)\n\nParticipants\n------------\n  Number of subjects: 54\n  Health status: healthy\n  Age: mean=29.5, min=24, max=35\n  Gender distribution: female=25, male=29\n  Handedness: right\n  BCI experience: mixed\n  Species: human\n\nExperimental Protocol\n---------------------\n  Paradigm: p300\n  Task type: copy_spelling\n  Number of classes: 2\n  Class labels: Target, NonTarget\n  Study design: 36-symbol ERP row-column speller with random-set presentation and face stimuli, offline training and online test phases\n  Feedback type: visual\n  Stimulus type: rc_speller\n  Stimulus modalities: visual\n  Primary modality: visual\n  Mode: offline\n  Training/test split: True\n  Instructions: Subjects were asked to copy-spell given sentences by gazing at target characters on screen. In training: 'NEURAL NETWORKS AND DEEP LEARNING' (33 characters), in test: 'PATTERN RECOGNITION MACHINE LEARNING' (36 characters). Participants counted number of times each target character flashed.\n\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: p300\n  Number of targets: 36\n  Number of repetitions: 5\n  Inter-stimulus interval: 135.0 ms\n  Stimulus onset asynchrony: 215.0 ms\n\nData Structure\n--------------\n  Trials: {'training': 1980, 'test': 2160}\n  Trials context: Training: copy-spell 'NEURAL NETWORKS AND DEEP LEARNING' (33 characters). Test: copy-spell 'PATTERN RECOGNITION MACHINE LEARNING' (36 characters). Each character received 5 sequences of 12 flashes (60 flashes total).\n\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\n\nSignal Processing\n-----------------\n  Classifiers: LDA\n  Feature extraction: Mean Amplitudes\n\nCross-Validation\n----------------\n  Method: training-test split\n  Evaluation type: within_session, cross_session\n\nPerformance (Original Study)\n----------------------------\n  Accuracy: 96.7%\n  Accuracy Std: 0.05\n  Illiteracy Rate: 11.1\n\nBCI Application\n---------------\n  Applications: speller, communication\n  Online feedback: True\n\nTags\n----\n  Pathology: Healthy\n  Modality: Visual\n  Type: Perception\n\nDocumentation\n-------------\n  Description: EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy\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; Tel: +82-2-3290-3197; Fax: +82-2-3290-3583\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  Keywords: EEG datasets, brain-computer interface, event-related potential, steady-state visually evoked potential, motor-imagery, OpenBMI toolbox, BCI illiteracy\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). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. 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