{"dataset":{"id":"194","dataset_id":"nm000163","name":"c-VEP and Burst-VEP dataset from Castillos et al. 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(2023)\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"resource_type_general\": \"Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"sizes\": [\n    \"165.3 MB (15 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"description\": \"This dataset comprises code-modulated Visual Evoked Potential (c-VEP) EEG recordings from 12 healthy participants performing a 2-class reactive brain-computer interface task with 4 targets. The study compares novel burst c-VEP sequences with traditional m-sequences at two stimulus amplitude depths (100% and 40%), evaluating classification performance, calibration efficiency, and user experience. EEG was recorded at 500 Hz using a 32-channel BrainProducts system with convolutional neural network decoding achieving 95.6% accuracy.\",\n  \"methods_description\": \"Twelve healthy participants (mean age 30.6±7.1 years) completed an offline 2-class c-VEP protocol with 4 targets using a factorial within-subject design. EEG was recorded at 500 Hz using a BrainProducts LiveAmp 32-channel system with active electrodes in a standard 10-20 montage (reference: FCz, ground: FPz). Visual stimuli were presented on a 60 Hz Dell monitor. Burst codes consisted of brief aperiodic flashes (2-4 flashes/second, ~50ms duration, ≥200ms inter-burst interval), while m-sequences used Fibonacci-type LFSR with 132-frame subsequences. A CNN architecture with spatial (8×1, 16 filters), temporal (1×32, 8 filters), and 2D convolution (5×5, 4 filters) layers decoded EEG using 250ms sliding windows with 2ms stride. Classification employed sequential train/test splits with Pearson correlation for target selection, with calibration data ranging from 1-6 blocks (8.8-52.8 seconds).\",\n  \"keywords\": [\n    {\n      \"term\": \"code-modulated visual evoked potentials\"\n    },\n    {\n      \"term\": \"c-VEP\"\n    },\n    {\n      \"term\": \"Burst-VEP\"\n    },\n    {\n      \"term\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"convolutional neural networks\"\n    },\n    {\n      \"term\": \"reactive BCI\"\n    },\n    {\n      \"term\": \"visual stimulation\"\n    },\n    {\n      \"term\": \"visual evoked potentials\"\n    },\n    {\n      \"term\": \"amplitude depth\"\n    },\n    {\n      \"term\": \"stimulus intensity\"\n    }\n  ],\n  \"source_hash\": \"d8e3a6c4198472585c60767488d35d7ea14d4d60644c99772a76bd7720f739f6\"\n}","last_activity_at":"2026-03-23 04:42:03","source":null,"source_id":null,"subject_count":12,"modalities":"eeg","age_min":30.6,"age_max":30.6,"file_size":165335288,"total_files":15,"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.nm000163-blue)](https://doi.org/10.82901/nemar.nm000163)\n\n# c-VEP and Burst-VEP dataset from Castillos et al. (2023)\n\nc-VEP and Burst-VEP dataset from Castillos et al. (2023)\n\n## Dataset Overview\n\n- **Code**: CastillosBurstVEP100\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\n## Acquisition\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**: {'notch': {'freq': 50.0, 'bandwidth': 0.2, 'order': 16, 'type': 'IIR cut-band'}}\n- **Impedance threshold**: 25.0 kOhm\n- **Cap manufacturer**: BrainProducts\n- **Cap model**: Acticap\n- **Electrode type**: active\n\n## Participants\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\n## Experimental Protocol\n\n- **Paradigm**: cvep\n- **Task type**: target selection\n- **Number of classes**: 2\n- **Class labels**: 0, 1\n- **Trial duration**: 2.2 s\n- **Tasks**: visual attention, target selection\n- **Study design**: factorial within-subject\n- **Study domain**: BCI performance and user experience\n- **Feedback type**: none\n- **Stimulus type**: visual\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Synchronicity**: synchronous\n- **Mode**: offline\n- **Training/test split**: False\n- **Instructions**: Focus on cued targets sequentially in random order\n- **Stimulus presentation**: software=PsychoPy, monitor=Dell P2419HC, resolution=1920x1080, refresh_rate_hz=60\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\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\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: cvep\n- **Code type**: burst\n- **Number of targets**: 4\n- **Cue duration**: 0.5 s\n\n## Data 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 burst c-VEP at 100% amplitude\n\n## Preprocessing\n\n- **Data state**: raw\n\n## Signal Processing\n\n- **Classifiers**: Convolutional Neural Network (CNN), Pearson correlation\n- **Feature extraction**: CNN spatial filtering (8x1 kernel, 16 filters), CNN temporal filtering (1x32 kernel with dilation 2, 8 filters), CNN 2D convolution (5x5 kernel, 4 filters), sliding windows (250ms, 2ms stride)\n- **Frequency bands**: analyzed=[0.1, 40.0] Hz\n- **Spatial filters**: CNN 8x1 spatial convolution (16 filters)\n\n## Cross-Validation\n\n- **Method**: sequential train/test split\n- **Evaluation type**: offline classification, iterative calibration (1-6 blocks)\n\n## Performance (Original Study)\n\n- **Accuracy**: 95.6%\n- **Itr**: 67.49 bits/min\n- **Selection Time S**: 1.5\n- **Cnn Training Time S**: 15.0\n- **Burst 40 Accuracy**: 94.2\n- **Mseq 100 Accuracy**: 85.0\n\n## BCI Application\n\n- **Applications**: reactive BCI\n- **Environment**: controlled laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: EEG\n- **Type**: reactive BCI, c-VEP, visual evoked potentials\n\n## Documentation\n\n- **Description**: Burst c-VEP based BCI study comparing novel burst code sequences to traditional m-sequences at two amplitude depths (100% and 40%) to optimize classification performance, minimize calibration data, and improve 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- **Funding**: AID (Powerbrain project), France; AXA Research Fund Chair for Neuroergonomics, France; Chair for Neuroadaptive Technology, Artificial and Natural Intelligence Toulouse Institute (ANITI), France\n- **Ethics approval**: University of Toulouse ethics committee (CER approval number 2020-334); Declaration of Helsinki\n- **Acknowledgements**: This work was funded by AID (Powerbrain project), France, the AXA Research Fund Chair for Neuroergonomics, France and Chair for Neuroadaptive Technology, Artificial and Natural Intelligence Toulouse Institute (ANITI), France.\n- **Keywords**: Code-VEP, Reactive BCI, CNN, Amplitude depth reduction, Visual comfort\n\n## External Links\n\n- **Source**: https://zenodo.org/record/8255618\n- **Github**: https://github.com/neuroergoISAE/burst_codes\n\n## Abstract\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 Burst c-VEP, an innovative variant involving short bursts of aperiodic visual flashes at 2-4 flashes per second. The proposed burst c-VEP sequences exhibited higher accuracy (90.5%-95.6%) compared to m-sequence counterparts (71.4%-85.0%) with mean selection time of 1.5s. Reducing stimulus intensity to 40% amplitude depth only slightly decreased accuracy to 94.2% while substantially improving user experience. The collected dataset and CNN architecture implementation are shared through open-access repositories.\n\n## Methodology\n\nTwelve healthy participants completed an offline 4-class c-VEP protocol using a factorial design. EEG was recorded at 500 Hz using BrainProducts LiveAmp 32-channel system. Participants focused on cued targets with factorial manipulation of pattern type (burst vs m-sequence) and amplitude depth (100% vs 40%). Visual stimuli were presented on a 60 Hz Dell monitor. Burst codes consisted of brief flashes (~50ms) with minimum 200ms inter-burst interval, while m-sequences used Fibonacci-type LFSR with segmented 132-frame subsequences. A CNN architecture with spatial (8x1, 16 filters), temporal (1x32, 8 filters), and 2D convolution (5x5, 4 filters) layers decoded EEG using 250ms sliding windows with 2ms stride. Calibration data ranged from 1-6 blocks (8.8-52.8s). Classification used sequential train/test splits with Pearson correlation for target selection. VEP analysis examined amplitude, latency, and inter-trial coherence. Statistical analyses used 2×2 repeated measures ANOVA.\n\n## References\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. 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