{"dataset":{"id":"49855","dataset_id":"nm000273","name":"OpenBMI SSVEP EEG dataset (Lee et al. 2019)","description":"A BIDS-formatted derivative dataset of steady-state visually evoked potential (SSVEP) EEG recordings from 54 subjects across 2 sessions, containing 21,600 trials recorded at 1000 Hz with 62 channels. This dataset, derived from the Lee et al. 2019 source dataset, has been standardized using the Mother of All BCI Benchmarks (MOABB) framework to enable reproducible benchmarking and analysis of brain-computer interface paradigms based on SSVEP stimulation.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000273","concept_doi":"10.82901/nemar.nm000273","latest_version_doi":"10.82901/nemar.nm000273.v1.0.7","created_at":"2026-06-20 18:42:05","updated_at":"2026-08-18 18:20:08","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"OpenBMI SSVEP EEG dataset (Lee et al. 2019)\",\n  \"description\": \"A BIDS-formatted derivative dataset of steady-state visually evoked potential (SSVEP) EEG recordings from 54 subjects across 2 sessions, containing 21,600 trials recorded at 1000 Hz with 62 channels. This dataset, derived from the Lee et al. 2019 source dataset, has been standardized using the Mother of All BCI Benchmarks (MOABB) framework to enable reproducible benchmarking and analysis of brain-computer interface paradigms based on SSVEP stimulation.\",\n  \"methods_description\": \"EEG data collected using SSVEP paradigm with 4 classes, 4-second trial length, 62-channel recording at 1000 Hz sampling frequency across 2 sessions from 54 subjects. Original data collection methods detailed in the primary publication (DOI: 10.1093/gigascience/giz002).\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"[Unspecified1]\": {},\n    \"[Unspecified2]\": {},\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\": \"steady-state visually evoked potential\"\n    },\n    {\n      \"term\": \"SSVEP\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"BIDS\"\n    },\n    {\n      \"term\": \"benchmark dataset\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.5524/100542\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000273\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1093/gigascience/giz002\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000273\",\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    \"77.7 GB (217 files)\"\n  ],\n  \"formats\": [\n    \".edf\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"8720df2c9b02aad1e12b0600c2b1ebff60c02e85128860629013bf6afc9ba63d\"\n}","last_activity_at":"2026-08-16 13:44:10","source":null,"source_id":null,"subject_count":54,"modalities":"eeg","age_min":null,"age_max":null,"file_size":77741113248,"total_files":1090,"tasks":"ssvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"[Unspecified1], [Unspecified2], Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000273-blue)](https://doi.org/10.82901/nemar.nm000273)\n\n# OpenBMI SSVEP EEG dataset (Lee et al. 2019)\n\n## Overview\n\nA derivative dataset of SSVEP (steady-state visually evoked potential) recordings processed and organized using the Mother of All BCI Benchmarks (MOABB) framework. This dataset represents EEG data formatted according to BIDS standards, enabling standardized analysis and benchmarking of brain-computer interface paradigms based on steady-state visual stimulation. The dataset is derived from the Lee2019 source dataset (DOI: 10.5524/100542) and has been converted to BIDS format using MNE-BIDS tools. The dataset contains EEG recordings from multiple subjects across multiple sessions with SSVEP stimulation paradigms.\n\n## Dataset Summary\n\n| Property | Value |\n|---|---|\n| Subjects | 54 |\n| Channels | 62 |\n| Classes | 4 |\n| Trial length | 4 s |\n| Sampling frequency | 1000 Hz |\n| Sessions | 2 |\n| Total trials | 21600 |\n| Paradigm | SSVEP |\n\n## Data Collection Methods\n\nSee the primary publication for details on data collection and experimental protocol.\n\n## How to Access via MOABB\n\nInstall MOABB and load this dataset directly:\n\n```python\nfrom moabb.datasets import Lee2019_SSVEP\nfrom moabb.paradigms import SSVEP\nparadigm = SSVEP()\n\ndataset = Lee2019_SSVEP()\nX, y, metadata = paradigm.get_data(dataset)\n```\n\nFor more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the\n[MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Lee2019_SSVEP.html).\n\n## Citation\n\nIf you use this dataset please cite the primary publication:\n\n> DOI: [10.1093/gigascience/giz002](https://doi.org/10.1093/gigascience/giz002)\n\n## NEMAR / MOABB Benchmark Collection\n\nThis BIDS-formatted dataset was converted from the original data using the\n[MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on\n[NEMAR](https://nemar.org/) as part of the MOABB benchmark collection.\nThe original data and license terms apply — see `dataset_description.json` for details.\n","bids_version":"1.7.0","sessions_count":2,"publish_date":null,"embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-08-22 07:33:38","zarr_store_count":108,"zarr_index_etag":"c14c3cca4f62f10a14e251551e4ab9d7","zarr_source_commit":"da857bdec67849bec8076cfcff899868477b1c3c","archive_status":"ready","archive_size":73182876511,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":100,"n_channels":62,"electrode_system":"10-05","has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":77739709446,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":1090,"zarr_pool_breaks":null,"total_recording_duration":146239,"recording_duration_min":1249,"recording_duration_max":1561,"recording_count":108,"recordings_unavailable":0,"recordings_measured":108,"channel_count_min":62,"channel_count_max":62,"sampling_frequency":1000,"power_line_frequency":50,"eeg_reference":null,"placement_scheme":"based on the extended 10/20 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:17:00\",\"metadata_updated_at\":\"2026-08-18 18:20:07\",\"archive_checked_at\":\"2026-08-18 19:11:45\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-08-18 18:24:00\",\"citations_updated_at\":\"2026-09-08 03:00:46\",\"channel_montage_checked_at\":\"2026-06-28 23:02:54\",\"hed_checked_at\":\"2026-06-30 04:33:07\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-22 03:01:34\",\"recording_stats_at\":\"2026-09-02 11:32:08\",\"signal_defaults_at\":\"2026-09-02 11:50:56\"}","participants":54,"num_citations":100,"latest_version":"v1.0.7","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"72.40 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000273/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}