{"dataset":{"id":"128","dataset_id":"nm000125","name":"Lee2021 – SSVEP paradigm of the Mobile BCI dataset","description":"This dataset comprises steady-state visually evoked potential (SSVEP) recordings from 23 healthy participants performing a brain-computer interface task during various locomotor states (standing, walking, running). The study includes 73-channel EEG data acquired at 100 Hz across 4 sessions per subject, with visual stimuli presented at three frequencies (5.45, 8.57, and 12.0 Hz). The dataset is designed to evaluate mobile BCI performance and the feasibility of SSVEP-based control during dynamic physical activity. This is a BIDS-formatted derivative of the original dataset published by Lee et al. (2021) and available at https://osf.io/r7s9b/.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000125","concept_doi":"10.82901/nemar.nm000125","latest_version_doi":"10.82901/nemar.nm000125.v1.0.2","created_at":"2026-03-06 21:25:43","updated_at":"2026-08-18 18:13:45","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Lee2021 – SSVEP paradigm of the Mobile BCI dataset\",\n  \"description\": \"This dataset comprises steady-state visually evoked potential (SSVEP) recordings from 23 healthy participants performing a brain-computer interface task during various locomotor states (standing, walking, running). The study includes 73-channel EEG data acquired at 100 Hz across 4 sessions per subject, with visual stimuli presented at three frequencies (5.45, 8.57, and 12.0 Hz). The dataset is designed to evaluate mobile BCI performance and the feasibility of SSVEP-based control during dynamic physical activity. This is a BIDS-formatted derivative of the original dataset published by Lee et al. (2021) and available at https://osf.io/r7s9b/.\",\n  \"methods_description\": \"EEG data were acquired using a 73-channel BrainAmp system (Brain Product GmbH) at 100 Hz sampling rate with Ag/AgCl electrodes referenced to FCz and grounded at Fpz. Participants performed SSVEP tasks with visual flicker stimuli at three frequencies (5.45, 8.57, and 12.0 Hz) during different locomotor conditions (standing, walking, running). Each trial lasted 5 seconds, with 20 trials per class across 4 sessions. Signal processing included power spectral analysis across frequency bands (delta, theta, alpha, beta) and classification using regularized Linear Discriminant Analysis (rLDA) and Canonical Correlation Analysis (CCA).\",\n  \"license\": \"CC BY 4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Young-Eun Lee\": {\n      \"orcid\": \"0000-0003-2610-7028\"\n    },\n    \"Gi-Hwan Shin\": {\n      \"orcid\": \"0000-0002-7348-0610\"\n    },\n    \"Minji Lee\": {\n      \"orcid\": \"0000-0003-4261-875X\"\n    },\n    \"Seong-Whan Lee\": {\n      \"orcid\": \"0000-0002-6249-4996\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"SSVEP\"\n    },\n    {\n      \"term\": \"EEG\"\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\": \"mobile BCI\"\n    },\n    {\n      \"term\": \"locomotion\"\n    },\n    {\n      \"term\": \"visual evoked potentials\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1038/s41597-021-01094-4\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000125\",\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\": \"https://nemar.org/dataset/nm000125\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"IITP\",\n      \"award_number\": \"2017-0-00451\"\n    },\n    {\n      \"funder_name\": \"IITP\",\n      \"award_number\": \"2015-0-00185\"\n    },\n    {\n      \"funder_name\": \"IITP\",\n      \"award_number\": \"2019-0-00079\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.4 GB (153 files)\"\n  ],\n  \"formats\": [\n    \".html\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".vhdr\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"f8a81ca1431e83d3bc6dcbecf00cfd02a6006d4c36cf6a8db068297358590df9\"\n}","last_activity_at":"2026-08-16 13:24:27","source":null,"source_id":null,"subject_count":23,"modalities":"eeg","age_min":19,"age_max":32,"file_size":1407767620,"total_files":759,"tasks":"SSVEP,ssvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee","license":"CC BY 4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000125-blue)](https://doi.org/10.82901/nemar.nm000125)\n\n# SSVEP paradigm of the Mobile BCI dataset\n\nSSVEP paradigm of the Mobile BCI dataset.\n\n## Dataset Overview\n\n- **Code**: Lee2021Mobile-SSVEP\n- **Paradigm**: ssvep\n- **DOI**: 10.1038/s41597-021-01094-4\n- **Subjects**: 23\n- **Sessions per subject**: 4\n- **Events**: 5.45=11, 8.57=12, 12.0=13\n- **Trial interval**: [0, 5] s\n- **File format**: BrainVision\n\n## Acquisition\n\n- **Sampling rate**: 100.0 Hz\n- **Number of channels**: 73\n- **Channel types**: eeg=73\n- **Montage**: standard_1005\n- **Hardware**: BrainAmp (Brain Product GmbH)\n- **Reference**: FCz\n- **Ground**: Fpz\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 60.0 Hz\n- **Impedance threshold**: 50 kOhm\n- **Electrode material**: Ag/AgCl\n- **Auxiliary channels**: EOG (4 ch, vertical, horizontal)\n\n## Participants\n\n- **Number of subjects**: 23\n- **Health status**: healthy\n- **Age**: mean=24.5, std=2.9, min=19, max=32\n- **Gender distribution**: male=13, female=10\n\n## Experimental Protocol\n\n- **Paradigm**: ssvep\n- **Number of classes**: 3\n- **Class labels**: 5.45, 8.57, 12.0\n- **Trial duration**: 5.0 s\n- **Study design**: BCI during motion (standing/walking/running)\n- **Stimulus type**: visual flicker\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  5.45\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/5_45\n\n  8.57\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/8_57\n\n  12.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12_0\n\n```\n## Signal Processing\n\n- **Classifiers**: rLDA, CCA\n- **Feature extraction**: power_over_time_intervals, CCA\n- **Frequency bands**: delta=[0.5, 3.5] Hz; theta=[3.5, 7.5] Hz; alpha=[7.5, 12.5] Hz; beta=[12.5, 30.0] Hz\n\n## Cross-Validation\n\n- **Method**: holdout\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: mobile_BCI\n- **Environment**: treadmill\n\n## Tags\n\n- **Pathology**: healthy\n- **Modality**: visual\n- **Type**: perception\n\n## Documentation\n\n- **DOI**: 10.1038/s41597-021-01094-4\n- **License**: CC BY 4.0\n- **Investigators**: Young-Eun Lee, Gi-Hwan Shin, Minji Lee, Seong-Whan Lee\n- **Senior author**: Seong-Whan Lee\n- **Institution**: Korea University\n- **Country**: KR\n- **Repository**: OSF\n- **Data URL**: https://osf.io/r7s9b/\n- **Publication year**: 2021\n- **Funding**: IITP No. 2017-0-00451; IITP No. 2015-0-00185; IITP No. 2019-0-00079\n- **Ethics approval**: Institutional Review Board of Korea University, KUIRB-2019-0194-01\n- **Keywords**: SSVEP, ERP, mobile BCI, ear-EEG, locomotion\n\n## References\n\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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n\n---\nGenerated by MOABB 1.4.3 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb\n","bids_version":"1.9.0","sessions_count":8,"publish_date":"2026-05-04 12:58:08","embedding_dirty":0,"license_tier":"attribution","zarr_status":"ready","zarr_converted_at":"2026-09-04 15:19:13","zarr_store_count":85,"zarr_index_etag":"ec2e833d0f6e02260fb91f947e016084","zarr_source_commit":"63d1b22364a36561c83f82cc30f1e5afebdec570","archive_status":"ready","archive_size":1307005364,"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":34,"n_channels":73,"electrode_system":"other","has_hed":1,"hed_version":"8.4.0","is_exemplar":0,"bytes_present":1407003805,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":1,"archive_absent_files":0,"archive_declared_files":759,"zarr_pool_breaks":0,"total_recording_duration":48010.40000000001,"recording_duration_min":497.72,"recording_duration_max":857.29,"recording_count":85,"recordings_unavailable":0,"recordings_measured":85,"channel_count_min":46,"channel_count_max":73,"sampling_frequency":100,"power_line_frequency":50,"eeg_reference":"FCz","placement_scheme":"10-05 system","sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-18 18:11:27\",\"metadata_updated_at\":\"2026-08-18 18:13:44\",\"archive_checked_at\":\"2026-08-18 18:21:38\",\"zarr_checked_at\":\"2026-06-07 17:58:18\",\"records_checked_at\":\"2026-08-18 18:20:48\",\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":\"2026-06-28 22:50:24\",\"hed_checked_at\":\"2026-06-30 04:09:41\",\"data_checked_at\":null,\"availability_report_at\":\"2026-08-19 03:00:18\",\"signal_defaults_at\":\"2026-09-02 11:36:41\",\"recording_stats_at\":\"2026-09-05 03:00:29\",\"zarr_verify_attempted_at\":\"2026-09-06 03:01:15\",\"zarr_verified_at\":\"2026-09-06 03:01:15\",\"zarr_verified_commit\":\"63d1b22364a36561c83f82cc30f1e5afebdec570\",\"zarr_verify_status\":\"verified\",\"zarr_verify_examples\":[],\"zarr_verify_sampled\":40.0,\"zarr_verify_checked\":40.0,\"zarr_verify_checked_channels\":40.0,\"zarr_verify_checked_duration\":40.0,\"zarr_verify_checked_rate\":40.0,\"zarr_verify_unchecked\":0.0,\"zarr_verify_mismatch_count\":0.0,\"zarr_verify_examples_truncated\":0.0}","participants":23,"num_citations":34,"latest_version":"v1.0.2","zarr_verify_status":"verified","zarr_verified_at":"2026-09-06 03:01:15","owner_username":"bruaristimunha","owner_github":"bruAristimunha","file_size_formatted":"1.31 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/nm000125/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}}