{"dataset":{"id":"377","dataset_id":"nm000342","name":"CastillosCVEP40","description":"A code-modulated Visual Evoked Potential (c-VEP) electroencephalography dataset from 12 healthy participants investigating burst c-VEP as a reactive brain-computer interface paradigm. This dataset contains m-sequence c-VEP data at 40% amplitude modulation depth. The original study employed a factorial design manipulating stimulus pattern (burst vs. m-sequence) and amplitude depth (100% vs. 40% modulation) to optimize classification accuracy while improving user visual comfort. Data were acquired at 500 Hz using 32-channel EEG with convolutional neural network classification achieving up to 95.6% accuracy on the full amplitude burst condition.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000342","concept_doi":"10.82901/nemar.nm000342","latest_version_doi":"10.82901/nemar.nm000342.v1.0.2","created_at":"2026-03-28 17:30:46","updated_at":"2026-08-18 21:24:04","zenodo_concept_id":"20527307","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"CastillosCVEP40\",\n  \"description\": \"A code-modulated Visual Evoked Potential (c-VEP) electroencephalography dataset from 12 healthy participants investigating burst c-VEP as a reactive brain-computer interface paradigm. This dataset contains m-sequence c-VEP data at 40% amplitude modulation depth. The original study employed a factorial design manipulating stimulus pattern (burst vs. m-sequence) and amplitude depth (100% vs. 40% modulation) to optimize classification accuracy while improving user visual comfort. Data were acquired at 500 Hz using 32-channel EEG with convolutional neural network classification achieving up to 95.6% accuracy on the full amplitude burst condition.\",\n  \"methods_description\": \"Factorial experimental design with 12 healthy participants (mean age 30.6±7.1 years). EEG recorded using BrainProducts LiveAmp 32-channel system at 500 Hz sampling rate with active electrodes in standard 10-20 montage, referenced to FCz. Four experimental conditions: burst vs. m-sequence patterns × 100% vs. 40% amplitude depth modulation. Each condition comprised 15 blocks of 4 trials. Trial structure: 0.5s visual cue (red-bordered square), 2.2s stimulation phase with aperiodic visual flashes on Dell monitor (60 Hz, 1920×1080), 0.7s inter-trial interval. Online 50 Hz notch filter (IIR, order 16). Preprocessing: average re-reference, IIR notch filtering, epoching (0-2.2s), baseline removal. Classification via convolutional neural network with sliding window bitwise decoding.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Kalou Cabrera Castillos\": {\n      \"orcid\": \"0009-0007-3501-5885\"\n    },\n    \"Simon Ladouce\": {\n      \"orcid\": \"0000-0001-6760-6240\"\n    },\n    \"Ludovic Darmet\": {\n      \"orcid\": \"0000-0001-5445-9763\"\n    },\n    \"Frédéric Dehais\": {}\n  },\n  \"keywords\": [\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\": \"visual evoked potentials\"\n    },\n    {\n      \"term\": \"code-VEP\"\n    },\n    {\n      \"term\": \"convolutional neural networks\"\n    },\n    {\n      \"term\": \"reactive BCI\"\n    },\n    {\n      \"term\": \"visual stimulation\"\n    },\n    {\n      \"term\": \"visual comfort\"\n    },\n    {\n      \"term\": \"amplitude modulation\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1016/j.neuroimage.2023.120446\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000342\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.5281/zenodo.8255618\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsVersionOf\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000342\",\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    \"354.3 MB (37 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".fdt\",\n    \".json\",\n    \".md\",\n    \".set\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"177476b579036035aafea8287dd89f367f5acf9777feb6ff7847380dfe172630\"\n}","last_activity_at":"2026-08-16 14:08:05","source":null,"source_id":null,"subject_count":12,"modalities":"eeg","age_min":30.6,"age_max":30.6,"file_size":356320823,"total_files":167,"tasks":"cvep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000342-blue)](https://doi.org/10.82901/nemar.nm000342)\n\nCastillosCVEP40\n===============\n\nc-VEP and Burst-VEP dataset from Castillos et al. (2023)\n\nDataset Overview\n----------------\n  Code: CastillosCVEP40\n  Paradigm: cvep\n  DOI: https://doi.org/10.1016/j.neuroimage.2023.120446\n  Subjects: 12\n  Sessions per subject: 1\n  Events: 0=100, 1=101\n  Trial interval: (0, 0.25) s\n  File format: EEGLAB .set\n  Number of contributing labs: 1\n\nAcquisition\n-----------\n  Sampling rate: 500.0 Hz\n  Number of channels: 32\n  Channel types: eeg=32\n  Channel names: C3, C4, CP1, CP2, CP5, CP6, Cz, F10, F3, F4, F7, F8, F9, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, O1, O2, Oz, P10, P3, P4, P7, P8, P9, Pz, T7, T8\n  Montage: standard_1020\n  Hardware: BrainProducts LiveAmp 32\n  Reference: FCz\n  Ground: FPz\n  Sensor type: EEG\n  Line frequency: 50.0 Hz\n  Online filters: {'line_noise_filter': 'IIR cut-band filter 49.9-50.1 Hz, order 16'}\n  Impedance threshold: 25.0 kOhm\n  Cap manufacturer: BrainProducts\n  Cap model: Acticap\n  Electrode type: active\n\nParticipants\n------------\n  Number of subjects: 12\n  Health status: healthy\n  Age: mean=30.6, std=7.1\n  Gender distribution: female=4, male=8\n  Species: human\n\nExperimental Protocol\n---------------------\n  Paradigm: cvep\n  Task type: reactive BCI\n  Number of classes: 2\n  Class labels: 0, 1\n  Trial duration: 2.2 s\n  Tasks: visual_attention\n  Study design: factorial design\n  Study domain: brain-computer interface\n  Feedback type: none\n  Stimulus type: visual flicker\n  Stimulus modalities: visual\n  Primary modality: visual\n  Synchronicity: synchronous\n  Mode: offline\n  Training/test split: False\n  Instructions: focus on targets that were cued sequentially in a random order for 0.5 s, followed by a 2.2 s stimulation phase\n  Stimulus presentation: cue_duration=500 ms, stimulation_duration=2200 ms, inter_trial_interval=700 ms, cue_type=red-bordered square around target stimulus, display=Dell P2419HC, 1920×1080 pixels, 265 cd/m², 60 Hz\n\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n  0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_0\n\n  1\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_1\n\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: cvep\n  Code type: m-sequence\n  Number of targets: 4\n  Cue duration: 0.5 s\n\nData Structure\n--------------\n  Trials: 60\n  Blocks per session: 15\n  Trials context: 15 blocks x 4 trials per block = 60 trials per subject for m-sequence c-VEP at 40% amplitude\n\nPreprocessing\n-------------\n  Data state: raw\n\nSignal Processing\n-----------------\n  Classifiers: CNN (Convolutional Neural Network)\n  Feature extraction: sliding windows, bitwise decoding\n\nCross-Validation\n----------------\n  Evaluation type: offline\n\nPerformance (Original Study)\n----------------------------\n  Accuracy: 95.6%\n  Burst 100 Accuracy 17.6S Calibration: 90.5\n  Burst 100 Accuracy 52.8S Calibration: 95.6\n  Burst 40 Accuracy: 94.2\n  Mseq 100 Accuracy 17.6S Calibration: 71.4\n  Mseq 100 Accuracy 52.8S Calibration: 85.0\n  Mean Selection Time: 1.5\n\nBCI Application\n---------------\n  Applications: reactive BCI\n  Environment: laboratory\n  Online feedback: False\n\nTags\n----\n  Pathology: Healthy\n  Modality: EEG\n  Type: reactive, code-VEP, visual\n\nDocumentation\n-------------\n  Description: Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience\n  DOI: 10.1016/j.neuroimage.2023.120446\n  Associated paper DOI: 10.1016/j.neuroimage.2023.120446\n  License: CC-BY-4.0\n  Investigators: Kalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais\n  Senior author: Frédéric Dehais\n  Contact: kalou.cabrera-castillos@isae-supaero.fr\n  Institution: Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)\n  Department: Human Factors and Neuroergonomics\n  Address: 10 Av. Edouard Belin, Toulouse, 31400, France\n  Country: FR\n  Repository: Zenodo\n  Data URL: https://zenodo.org/record/8255618\n  Publication year: 2023\n  Ethics approval: University of Toulouse CER approval number 2020-334\n  Keywords: Code-VEP, Reactive BCI, CNN, Amplitude depth reduction, Visual comfort\n\nExternal Links\n--------------\n  Source: https://zenodo.org/record/8255618\n\nAbstract\n--------\nThe utilization of aperiodic flickering visual stimuli under the form of code-modulated Visual Evoked Potentials (c-VEP) represents a pivotal advancement in the field of reactive Brain–Computer Interface (rBCI). This study introduces an innovative variant of code-VEP, referred to as 'Burst c-VEP', involving the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate (2-4 flashes per second). The study tested an offline 4-classes c-VEP protocol involving 12 participants with factorial design manipulating pattern (burst and m-sequences) and amplitude (100% or 40% depth modulations). Full amplitude burst c-VEP sequences exhibited higher accuracy (90.5% with 17.6s calibration to 95.6% with 52.8s calibration) compared to m-sequence (71.4% to 85.0%). Mean selection time was 1.5s. Lowering intensity to 40% decreased accuracy slightly to 94.2% while improving user experience substantially.\n\nMethodology\n-----------\nFactorial experimental design with 12 participants. Four conditions: burst vs m-sequence × 100% vs 40% amplitude depth. Participants seated comfortably, presented with 15 blocks of 4 trials for each condition. Each trial: 0.5s cue (red-bordered square), 2.2s stimulation, 0.7s inter-trial interval. Four disc targets (150 pixels) on Dell monitor (60 Hz). Background: medium grey (50% max luminance, 124 lux). 100% condition: modulation to brightest white (168 lux). 40% condition: 40% of grey-to-white range (142 lux). EEG recorded with BrainProducts LiveAmp (32 channels, 500 Hz), impedance <25kΩ. Analysis on subset: O1, O2, Oz, Pz, P3, P4, P8, P9. Preprocessing: average re-reference, IIR notch filter (49.9-50.1 Hz, order 16), epoching (0-2.2s), baseline removal. Classification: CNN architecture with sliding windows for bitwise decoding.\n\nReferences\n----------\nKalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset). DOI: https://doi.org/10.5281/zenodo.8255618\n\nKalou Cabrera Castillos, Simon Ladouce, Ludovic Darmet, Frédéric Dehais. Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience,NeuroImage,Volume 284, 2023,120446,ISSN 1053-8119 DOI: https://doi.org/10.1016/j.neuroimage.2023.120446\n\nNotes\n\n.. versionadded:: 1.1.0\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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