{"dataset":{"id":"272","dataset_id":"nm000239","name":"P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)","description":"This dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study investigates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI paradigm development and evaluation.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000239","concept_doi":"10.82901/nemar.nm000239","latest_version_doi":"10.82901/nemar.nm000239.v1.0.4","created_at":"2026-03-25 20:24:03","updated_at":"2026-08-18 21:20:19","zenodo_concept_id":"20668867","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)\",\n  \"description\": \"This dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study investigates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI paradigm development and evaluation.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz sampling rate using 16 channels arranged in a standard 1005 montage with 50 Hz line frequency. Participants completed 5 sessions, each containing 8 runs (6 training, 2 testing) of the c-VEP paradigm with 11 visual stimulus classes based on p-ary m-sequences. Trial intervals were 1 second in duration.\",\n  \"license\": \"CC-BY-NC-SA-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\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    \"Sergio Pérez-Velasco\": {\n      \"orcid\": \"0000-0002-2999-3216\"\n    },\n    \"Diego Marcos-Martínez\": {\n      \"orcid\": \"0000-0002-7493-5242\"\n    },\n    \"Selene Moreno-Calderón\": {\n      \"orcid\": \"0000-0001-8605-6336\"\n    },\n    \"Roberto Hornero\": {\n      \"orcid\": \"0000-0001-9915-2570\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"code-modulated visual evoked potentials\"\n    },\n    {\n      \"term\": \"m-sequences\"\n    },\n    {\n      \"term\": \"p-ary m-sequences\"\n    },\n    {\n      \"term\": \"visual stimulation\"\n    },\n    {\n      \"term\": \"BCI paradigm\"\n    },\n    {\n      \"term\": \"non-binary stimulation\"\n    },\n    {\n      \"term\": \"MOABB\"\n    },\n    {\n      \"term\": \"benchmark\"\n    },\n    {\n      \"term\": \"Evoked Potentials, Visual\",\n      \"subject_scheme\": \"MeSH\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.71569/025s-eq10\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1016/j.eswa.2023.120815\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000239\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1088/1741-2552/ac38cf\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\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\": \"10.35376/10324/70945\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000239\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"CIBER-BBN through Instituto de Salud Carlos III\"\n    },\n    {\n      \"funder_name\": \"Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación\",\n      \"award_number\": \"TED2021-129915B-I00\"\n    },\n    {\n      \"funder_name\": \"Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación\",\n      \"award_number\": \"RTC2019-007350-1\"\n    },\n    {\n      \"funder_name\": \"Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación\",\n      \"award_number\": \"PID2020-115468RB-I00\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.8 GB (657 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\",\n    \".zip\"\n  ],\n  \"source_hash\": \"d6d1db475cb596134754601c3282f28337c41c4999918cedbdcdf83459e00c67\"\n}","last_activity_at":"2026-08-16 13:41:10","source":null,"source_id":null,"subject_count":16,"modalities":"eeg","age_min":null,"age_max":null,"file_size":1833712679,"total_files":4267,"tasks":"cvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, Diego Marcos-Martínez, Selene Moreno-Calderón, Roberto Hornero","license":"CC-BY-NC-SA-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000239-blue)](https://doi.org/10.82901/nemar.nm000239)\n\n# P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)\n\nP-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)\n\n## Dataset Overview\n\n- **Code**: MartinezCagigal2023Parycvep\n- **Paradigm**: cvep\n- **DOI**: https://doi.org/10.71569/025s-eq10\n- **Subjects**: 16\n- **Sessions per subject**: 5\n- **Events**: 0.0=100, 1.0=101, 2.0=102, 3.0=103, 4.0=104, 5.0=105, 6.0=106, 7.0=107, 8.0=108, 9.0=109, 10.0=110\n- **Trial interval**: (0, 1) s\n- **Runs per session**: 8\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**: 11\n- **Class labels**: 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0\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  2.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_2_0\n\n  3.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_3_0\n\n  4.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_4_0\n\n  5.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_5_0\n\n  6.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_6_0\n\n  7.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_7_0\n\n  8.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_8_0\n\n  9.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_9_0\n\n  10.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_10_0\n\n```\n## Documentation\n\n- **DOI**: 10.71569/025s-eq10\n- **Associated paper DOI**: 10.1016/j.eswa.2023.120815\n- **License**: CC-BY-NC-SA-4.0\n- **Investigators**: Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, Diego Marcos-Martínez, Selene Moreno-Calderón, 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/10.71569/025s-eq10\n- **Publication year**: 2023\n- **Funding**: Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación and ERDF (TED2021-129915B-I00, RTC2019-007350-1, PID2020-115468RB-I00); CIBER-BBN through Instituto de Salud Carlos III\n- **Ethics approval**: Approved by the local ethics committee; all participants provided informed consent\n- **Acknowledgements**: This study was partially funded by Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación and ERDF, and CIBER-BBN through Instituto de Salud Carlos III.\n- **How to acknowledge**: Please cite: Martínez-Cagigal et al. (2023). Non-binary m-sequences for more comfortable brain-computer interfaces based on c-VEPs. Expert Systems With Applications, 232, 120815. https://doi.org/10.1016/j.eswa.2023.120815\n\n## References\n\nMartínez-Cagigal, V., Santamaría-Vázquez, E., Pérez-Velasco, S., Marcos-Martínez, D., Moreno-Calderón, S., & Hornero, R. (2023). Non-binary m-sequences for more comfortable brain-computer interfaces based on c-VEPs. *Expert Systems with Applications, 232*, 120815. https://doi.org/10.1016/j.eswa.2023.120815\n\nMartínez-Cagigal, V., Thielen, J., Santamaría-Vázquez, E., Pérez-Velasco, S., Desain, P., & Hornero, R. (2021). Brain-computer interfaces based on code-modulated visual evoked potentials (c-VEP): A literature review. *Journal of Neural Engineering*, 18(6), 061002. https://doi.org/10.1088/1741-2552/ac38cf\n\nMartínez-Cagigal, V. (2025). Dataset: Non-binary m-sequences for more comfortable brain-computer interfaces based on c-VEPs. https://doi.org/10.35376/10324/70945\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, eight runs are available (six for training, two 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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