{"dataset":{"id":"273","dataset_id":"nm000240","name":"Checkerboard m-sequence-based c-VEP dataset from","description":"A derivative EEG dataset containing checkerboard m-sequence-based code-modulated visual evoked potential (c-VEP) recordings from 16 healthy participants across 8 sessions. This dataset is derived from the source dataset by Martínez-Cagigal et al. (2025) (DOI: 10.71569/7c67-v596) and is described in the publication by Fernández-Rodríguez et al. (2023) in Frontiers in Human Neuroscience. The dataset evaluates the influence of spatial frequency in visual stimuli for brain-computer interface applications, with 16-channel EEG data sampled at 256 Hz during a two-class visual stimulation paradigm.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000240","concept_doi":"10.82901/nemar.nm000240","latest_version_doi":"10.82901/nemar.nm000240.v1.0.4","created_at":"2026-03-25 20:24:53","updated_at":"2026-08-18 21:20:37","zenodo_concept_id":"20520690","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Checkerboard m-sequence-based c-VEP dataset from\",\n  \"description\": \"A derivative EEG dataset containing checkerboard m-sequence-based code-modulated visual evoked potential (c-VEP) recordings from 16 healthy participants across 8 sessions. This dataset is derived from the source dataset by Martínez-Cagigal et al. (2025) (DOI: 10.71569/7c67-v596) and is described in the publication by Fernández-Rodríguez et al. (2023) in Frontiers in Human Neuroscience. The dataset evaluates the influence of spatial frequency in visual stimuli for brain-computer interface applications, with 16-channel EEG data sampled at 256 Hz during a two-class visual stimulation paradigm.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz sampling rate using 16 channels in a standard 1005 montage with 50 Hz line frequency. Participants completed 8 sessions with 3 runs per session (2 training, 1 testing), viewing checkerboard m-sequence-based visual stimuli in a two-class c-VEP paradigm.\",\n  \"license\": \"CC-BY-NC-SA-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Álvaro Fernández-Rodríguez\": {},\n    \"Víctor Martínez-Cagigal\": {\n      \"orcid\": \"0000-0002-3822-1787\",\n      \"affiliations\": [\n        {\n          \"name\": \"Universidad de Valladolid\",\n          \"identifier\": \"https://ror.org/01fvbaw18\",\n          \"scheme\": \"ROR\"\n        }\n      ]\n    },\n    \"Eduardo Santamaría-Vázquez\": {\n      \"orcid\": \"0000-0002-7688-4258\"\n    },\n    \"Ricardo Ron-Angevin\": {},\n    \"Roberto Hornero\": {\n      \"orcid\": \"0000-0001-9915-2570\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\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\": \"visual evoked potentials\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014793\"\n    },\n    {\n      \"term\": \"code-modulated VEP\"\n    },\n    {\n      \"term\": \"spatial frequency\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D013028\"\n    },\n    {\n      \"term\": \"checkerboard stimuli\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.71569/7c67-v596\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.3389/fnhum.2023.1288438\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000240\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1016/j.cmpb.2023.107357\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000240\",\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    \"1.5 GB (400 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\",\n    \".zip\"\n  ],\n  \"source_hash\": \"def03dc9a33e58cb992aee11f0b34799d4aaa79e4cf2f0bba554a488928e40f4\"\n}","last_activity_at":"2026-08-16 13:39:05","source":null,"source_id":null,"subject_count":16,"modalities":"eeg","age_min":null,"age_max":null,"file_size":1480173345,"total_files":2965,"tasks":"cvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Álvaro Fernández-Rodríguez, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Ricardo Ron-Angevin, Roberto Hornero","license":"CC-BY-NC-SA-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000240-blue)](https://doi.org/10.82901/nemar.nm000240)\n\n# Checkerboard m-sequence-based c-VEP dataset from\n\nCheckerboard m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2025) and Fernández-Rodríguez et al. (2023).\n\n## Dataset Overview\n\n- **Code**: MartinezCagigal2023Checkercvep\n- **Paradigm**: cvep\n- **DOI**: https://doi.org/10.71569/7c67-v596\n- **Subjects**: 16\n- **Sessions per subject**: 8\n- **Events**: 0.0=100, 1.0=101\n- **Trial interval**: (0, 1) s\n- **Runs per session**: 3\n\n## Acquisition\n\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 16\n- **Channel types**: eeg=16\n- **Montage**: standard_1005\n- **Line frequency**: 50.0 Hz\n\n## Participants\n\n- **Number of subjects**: 16\n- **Health status**: healthy\n\n## Experimental Protocol\n\n- **Paradigm**: cvep\n- **Number of classes**: 2\n- **Class labels**: 0.0, 1.0\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  0.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_0_0\n\n  1.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_1_0\n\n```\n## Documentation\n\n- **DOI**: 10.71569/7c67-v596\n- **Associated paper DOI**: 10.3389/fnhum.2023.1288438\n- **License**: CC-BY-NC-SA-4.0\n- **Investigators**: Álvaro Fernández-Rodríguez, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Ricardo Ron-Angevin, Roberto Hornero\n- **Senior author**: Roberto Hornero\n- **Contact**: victor.martinez@gib.tel.uva.es\n- **Institution**: University of Valladolid\n- **Department**: Biomedical Engineering Group, ETSIT\n- **Address**: Paseo de Belén, 15, 47011, Valladolid, Spain\n- **Country**: ES\n- **Repository**: U Valladoid\n- **Data URL**: https://doi.org/10.71569/7c67-v596\n- **Publication year**: 2023\n- **Ethics approval**: Approved by the local ethics committee; all participants provided informed consent\n- **How to acknowledge**: Please cite: Fernández-Rodríguez et al. (2023). Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. Frontiers in Human Neuroscience, 17, 1288438. https://doi.org/10.3389/fnhum.2023.1288438\n\n## References\n\nMartínez Cagigal, V. (2025). Dataset: Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. https://doi.org/10.71569/7c67-v596\n\nFernández-Rodríguez, Á., Martínez-Cagigal, V., Santamaría-Vázquez, E., Ron-Angevin, R., & Hornero, R. (2023). Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. Frontiers in Human Neuroscience, 17, 1288438. https://doi.org/10.3389/fnhum.2023.1288438\n\nSantamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., Rodríguez-González, V., Pérez-Velasco, S., Moreno-Calderón, S., & Hornero, R. (2023). MEDUSA©: A novel Python-based software ecosystem to accelerate brain–computer interface and cognitive neuroscience research. Computer Methods and Programs in Biomedicine, 230, 107357. https://doi.org/10.1016/j.cmpb.2023.107357\n\nNotes\n\nAlthough the dataset was recorded in a single session, each condition is stored as a separate session to match the MOABB structure. Within each session, three runs are available (two for training, one for testing).\n\n.. versionadded:: 1.2.0\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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