{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd7","dataset_id":"nm000323","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.nm000323","datatypes":["eeg"],"demographics":{"subjects_count":54,"ages":[29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29,29],"age_min":29,"age_max":29,"age_mean":29.0,"species":null,"sex_distribution":null,"handedness_distribution":{"r":54}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000323","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"2199f9a23114e38381bb36d7bc494859d542415ee9bff0b0e30e1a5e90506742","license":"GPL-3.0","n_contributing_labs":null,"name":"Lee et al. 2019 (ERP) — 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.nm000323-blue)](https://doi.org/10.82901/nemar.nm000323)\nLee2019-ERP\n===========\nBMI/OpenBMI dataset for P300.\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\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)\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\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.\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\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\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).\nPreprocessing\n-------------\n  Data state: raw\n  Preprocessing applied: False\nSignal Processing\n-----------------\n  Classifiers: LDA\n  Feature extraction: Mean Amplitudes\nCross-Validation\n----------------\n  Method: training-test split\n  Evaluation type: within_session, cross_session\nPerformance (Original Study)\n----------------------------\n  Accuracy: 96.7%\n  Accuracy Std: 0.05\n  Illiteracy Rate: 11.1\nBCI Application\n---------------\n  Applications: speller, communication\n  Online feedback: True\nTags\n----\n  Pathology: Healthy\n  Modality: Visual\n  Type: Perception\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\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":141388340508,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000323","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-08-26T11:04:43.928099+00:00","dataset_created_at":null,"dataset_modified_at":"2026-08-18 18:18:27"},"total_files":216,"computed_title":"Lee et al. 2019 (ERP) — 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":"2026-10-15T11:39:09.143244+00:00","total_duration_s":209248.784,"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":["Perception"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"The visual-discrimination example maps detection of visual evidence to Perception, despite a choice response. The Parkinson's and TBI oddball examples show that target-related paradigms can instead receive Attention when attentional processing is the study aim. The motor-imagery BCI example shows that BCI is not itself a Type label; the particular paradigm determines the label. Here, the P300 copy-speller uses visual target flashes, and the metadata explicitly identifies its Type as Perception.","metadata_analysis":"The readme states \"Health status: healthy\" and \"Subjects: 54.\" It describes a \"36-symbol ERP row-column speller with random-set presentation and face stimuli\" and says \"Stimulus modalities: visual\" and \"Primary modality: visual.\" Participants \"counted number of times each target character flashed\"; events are \"Target\" and \"NonTarget.\" Its supplied tags read \"Pathology: Healthy,\" \"Modality: Visual,\" and \"Type: Perception.\"","paper_abstract_analysis":"No paper abstract is provided. The cited publication title mentions BCI illiteracy, but does not establish a clinical recruitment condition or change the documented P300 task.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\" and \"Pathology: Healthy\"; the healthy-volunteer BCI example suggests Healthy. ALIGN. Modality: Metadata says \"Stimulus modalities: visual\" and describes characters that \"flashed\" on screen; the visually cued BCI and visual-discrimination examples suggest Visual. ALIGN. Type: Metadata says \"Type: Perception\" and documents visual \"Target\"/\"NonTarget\" flashes; the visual-discrimination example suggests Perception, while oddball examples make Attention a plausible alternative. PARTIAL ALIGN: the explicit task tag and visual target-detection description favor Perception. No metadata fact is overridden by a few-shot pattern.","decision_summary":"Pathology—Healthy versus Unknown: \"Health status: healthy,\" \"Pathology: Healthy,\" and the absence of a recruited clinical group favor Healthy (0.9). Modality—Visual versus Multisensory: \"Stimulus modalities: visual,\" \"Primary modality: visual,\" and the on-screen flashing characters favor Visual; no second stimulus channel is described (0.9). Type—Perception versus Attention: \"Type: Perception,\" \"Target\"/\"NonTarget\" visual presentations, and counting target flashes favor Perception. Attention is plausible for P300 target selection, but the documented task and explicit tag support Perception more strongly; copy-spelling mechanics do not make language processing the primary aim (0.9)."}},"canonical_name":null,"name_confidence":0.72,"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_ERP","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"}],"source_datasets":[{"DOI":"10.5524/100542"},{"DOI":"doi:10.1093/gigascience/giz002"}],"bad_channels_info":null,"n_records":216}}