{"success":true,"database":"eegdash","data":{"_id":"69d16e06897a7725c66f4ce0","dataset_id":"nm000342","associated_paper_doi":"10.1016/j.neuroimage.2023.120446","authors":["Kalou Cabrera Castillos","Simon Ladouce","Ludovic Darmet","Frédéric Dehais"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1016/j.neuroimage.2023.120446","datatypes":["eeg"],"demographics":{"subjects_count":12,"ages":[30,30,30,30,30,30,30,30,30,30,30,30],"age_min":30,"age_max":30,"age_mean":30.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1016/j.neuroimage.2023.120446"},"funding":[],"ingestion_fingerprint":"ab2e3068ed3766fda5e549029405f151d51a1c977a1ecd3d017e59ac93fc9c91","license":"CC-BY-4.0","n_contributing_labs":null,"name":"CastillosCVEP40","readme":"CastillosCVEP40\n===============\nc-VEP and Burst-VEP dataset from Castillos et al. (2023)\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\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\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\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\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_0\n  1\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/intensity_1\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: cvep\n  Code type: m-sequence\n  Number of targets: 4\n  Cue duration: 0.5 s\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\nPreprocessing\n-------------\n  Data state: raw\nSignal Processing\n-----------------\n  Classifiers: CNN (Convolutional Neural Network)\n  Feature extraction: sliding windows, bitwise decoding\nCross-Validation\n----------------\n  Evaluation type: offline\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\nBCI Application\n---------------\n  Applications: reactive BCI\n  Environment: laboratory\n  Online feedback: False\nTags\n----\n  Pathology: Healthy\n  Modality: EEG\n  Type: reactive, code-VEP, visual\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\nExternal Links\n--------------\n  Source: https://zenodo.org/record/8255618\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.\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.\nReferences\n----------\nKalou Cabrera Castillos. (2023). 4-class code-VEP EEG data [Data set]. Zenodo.(dataset). DOI: https://doi.org/10.5281/zenodo.8255618\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\nNotes\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\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---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0"],"size_bytes":152363973,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000342","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["cvep"],"timestamps":{"digested_at":"2026-04-30T14:09:56.464130+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-28T17:34:31Z"},"total_files":12,"computed_title":"CastillosCVEP40","nchans_counts":[{"val":32,"count":12}],"sfreq_counts":[{"val":500.0,"count":12}],"stats_computed_at":"2026-05-01T13:49:34.659783+00:00","total_duration_s":3055.9759999999997,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"The visual moving-dot discrimination example maps a visual-stimulus study to Visual and Perception, even though participants make responses. The motor-imagery BCI example shows that BCI mechanics alone do not determine Type; the research focus does. Here, the focus is on visual stimulus design and evoked responses, not movement.","metadata_analysis":"Population is explicit: \"Health status: healthy\" and \"Number of subjects: 12.\" Stimuli are explicit: \"Stimulus type: visual flicker\" and \"Stimulus modalities: visual.\" The protocol says participants should \"focus on targets that were cued sequentially,\" while the documentation describes \"Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience.\"","paper_abstract_analysis":"The abstract confirms that burst c-VEP manipulates visual-flash pattern and contrast to improve evoked-potential decoding and visual comfort. It supports Perception over Attention as the primary research construct, although attending to cued targets is necessary for the task.","evidence_alignment_check":"Pathology: metadata says \"Health status: healthy\"; healthy-volunteer few-shot examples suggest Healthy. ALIGN. Modality: metadata says \"Stimulus type: visual flicker\" and \"Stimulus modalities: visual\"; visual-task examples suggest Visual. ALIGN. Type: metadata describes \"visual comfort\" and stimulus-design optimization, with participants instructed to \"focus on targets\"; the visual-discrimination example suggests Perception, while cue-focused tasks make Attention plausible. PARTIAL ALIGN: Perception better captures the stated research aim; attention is a task requirement rather than the measured construct.","decision_summary":"Pathology—Healthy versus Unknown: explicit \"Health status: healthy\" and \"Number of subjects: 12\" support Healthy; no clinical recruitment is described. Confidence 0.8. Modality—Visual versus Multisensory: \"Stimulus type: visual flicker,\" \"Stimulus modalities: visual,\" and the red-bordered visual cue support Visual; no second sensory channel is described. Confidence 0.9. Type—Perception versus Attention: \"Optimizing stimulus design\" and \"visual comfort,\" together with manipulated visual contrast and visual evoked potentials, favor Perception. \"Focus on targets\" supports Attention as runner-up, but attention is not the stated research outcome. Confidence 0.8. No explicit metadata fact was overridden by a few-shot pattern."}},"canonical_name":null,"name_confidence":0.72,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Castillos2023_CastillosCVEP40","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":4,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}