{"success":true,"database":"eegdash","data":{"_id":"69d16e06897a7725c66f4ce3","dataset_id":"nm000345","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":"14f895227e9daee93d05ddde1d35b4baff5d26742650e870f7e2c9343661a699","license":"CC-BY-4.0","n_contributing_labs":null,"name":"CastillosBurstVEP40","readme":"CastillosBurstVEP40\n===================\nc-VEP and Burst-VEP dataset from Castillos et al. (2023)\nDataset Overview\n----------------\n  Code: CastillosBurstVEP40\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': 'IIR cut-band filter between 49.9 and 50.1 Hz of 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: attend to cued target\n  Study design: factorial design\n  Study domain: brain-computer interface\n  Feedback type: none\n  Stimulus type: aperiodic visual flashes\n  Stimulus modalities: visual\n  Primary modality: visual\n  Synchronicity: synchronous\n  Mode: offline\n  Training/test split: False\n  Instructions: Participants were instructed to focus on c-VEP targets cued sequentially\n  Stimulus presentation: screen=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  Stimulus frequencies: [2.0, 3.0, 4.0] Hz\n  Code type: burst\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 burst c-VEP at 40% amplitude\nPreprocessing\n-------------\n  Data state: raw\nSignal Processing\n-----------------\n  Classifiers: CNN, Convolutional Neural Network\n  Feature extraction: EEG2Code 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  Mseq 100 Accuracy 17.6S Calibration: 71.4\n  Mseq 100 Accuracy 52.8S Calibration: 85.0\n  Burst 40 Accuracy: 94.2\n  Mean Selection Time S: 1.5\nBCI Application\n---------------\n  Applications: brain-computer interface\n  Environment: laboratory\n  Online feedback: False\nTags\n----\n  Pathology: Healthy\n  Modality: EEG\n  Type: reactive BCI, c-VEP\nDocumentation\n-------------\n  Description: Burst c-VEP based BCI study optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience. The study introduces an innovative variant of code-VEP called 'Burst c-VEP' involving short bursts of aperiodic visual flashes at 2-4 flashes per second.\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 ethics committee (CER approval number 2020-334); Declaration of Helsinki\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). A major advantage of the c-VEP approach is that the training of the model is independent of the number and complexity of targets, which helps reduce calibration time. Nevertheless, the existing designs of c-VEP stimuli can be further improved in terms of visual user experience but also to achieve a higher signal-to-noise ratio, while shortening the selection time and calibration process. In this study, we introduce an innovative variant of code-VEP, referred to as 'Burst c-VEP'. This original approach involves the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate, typically ranging from two to four flashes per second. The rationale behind this design is to leverage the sensitivity of the primary visual cortex to transient changes in low-level stimuli features to reliably elicit distinctive series of visual evoked potentials. In comparison to other types of faster-paced code sequences, burst c-VEP exhibit favorable properties to achieve high bitwise decoding performance using convolutional neural networks (CNN), which yields potential to attain faster selection time with the need for less calibration data. Furthermore, our investigation focuses on reducing the perceptual saliency of c-VEP through the attenuation of visual stimuli contrast and intensity to significantly improve users' visual comfort. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. Following a factorial design, participants were instructed to focus on c-VEP targets whose pattern (burst and maximum-length sequences) and amplitude (100% or 40% amplitude depth modulations) were manipulated across experimental conditions. Firstly, the full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90.5% (with 17.6 s of calibration data) to 95.6% (with 52.8 s of calibration data), compared to its m-sequence counterpart (71.4% to 85.0%). The mean selection time for both types of codes (1.5 s) compared favorably to reports from previous studies. Secondly, our findings revealed that lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94.2% while leading to substantial improvements in terms of user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance reactive BCI both in terms of performance and usability. The collected dataset, along with the proposed CNN architecture implementation, are shared through open-access repositories.\nMethodology\n-----------\nFactorial experimental design with 12 participants. Four conditions: burst or m-sequence codes × 100% or 40% amplitude depth. Participants attended to cued targets presented as aperiodic visual flashes. Burst codes: 50ms flashes at 2-4 Hz with 200ms minimum inter-burst interval. M-sequences: pseudo-random binary sequences at ~10 Hz. EEG recorded at 500 Hz using 32-channel BrainProduct LiveAmp. Analysis on occipital/parietal electrodes. CNN-based bitwise decoding (improved EEG2Code architecture). Each participant completed 15 blocks of 4 trials per condition (60 trials per class, 240 total trials). Trial structure: 700ms ITI, 500ms cue, 2200ms stimulation. Display: Dell P2419HC 60Hz LCD. Luminance: medium grey background (124 lux), 100% condition (168 lux), 40% condition (142 lux). Preprocessing: average re-reference, 50Hz notch filter (IIR order 16), epoching 0-2.2s, baseline removal. Subjective assessments of visual comfort, tiredness, and intrusiveness collected.\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":151182693,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000345","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["cvep"],"timestamps":{"digested_at":"2026-04-30T14:09:56.831732+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-28T17:41:50Z"},"total_files":12,"computed_title":"CastillosBurstVEP40","nchans_counts":[{"val":32,"count":12}],"sfreq_counts":[{"val":500.0,"count":12}],"stats_computed_at":"2026-05-01T13:49:34.659821+00:00","total_duration_s":3031.9759999999997,"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":0.9},"reasoning":{"few_shot_analysis":"The music-and-speech auditory brainstem response example labels stimulus-evoked sensory responses as Perception; its modality follows the presented sounds, not the EEG recording. The visual discrimination example likewise maps visual stimuli and their processing to Visual and Perception. The cued motor-imagery BCI example is Motor because movement imagery is its research focus; BCI use alone does not imply Motor.","metadata_analysis":"The readme states \"Health status: healthy\" and \"Number of subjects: 12.\" It specifies \"Stimulus type: aperiodic visual flashes,\" \"Stimulus modalities: visual,\" and \"Tasks: attend to cued target.\" Its description calls this a \"Burst c-VEP based BCI study optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience.\" The abstract describes eliciting \"distinctive series of visual evoked potentials\" and reducing \"perceptual saliency\" to improve visual comfort.","paper_abstract_analysis":"The abstract confirms that participants focused on visual targets and that the study manipulated flash patterns and intensity to assess evoked responses, decoding, and visual comfort. It does not identify a clinical population or a motor-imagery task.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\"; the few-shot convention assigns Healthy to cohorts without a recruited disorder. ALIGN. Modality: Metadata says \"Stimulus modalities: visual\" and \"aperiodic visual flashes\"; the few-shot convention labels the stimulus channel Visual rather than the recording method. ALIGN. Type: Metadata says \"optimizing stimulus design\" and \"visual evoked potentials,\" while also saying \"attend to cued target\"; the sensory-response few shots suggest Perception, whereas the cue makes Attention plausible. These facts ALIGN with Perception as the stronger primary focus; no metadata fact is overridden.","decision_summary":"Pathology—Healthy versus Unknown: \"Health status: healthy\" and \"Pathology: Healthy\" directly support Healthy; the 12-subject cohort has no stated clinical recruitment. Healthy wins (0.8). Modality—Visual versus Multisensory: \"aperiodic visual flashes,\" \"Stimulus modalities: visual,\" and \"Visual-presentation\" support Visual; no second sensory channel is described. Visual wins (0.9). Type—Perception versus Attention: \"visual evoked potentials,\" \"optimizing stimulus design,\" and \"perceptual saliency\" support Perception, consistent with the sensory-response few-shot example. \"Attend to cued target\" supports Attention, but describes how participants performed the task rather than a separate primary research aim. Perception wins (0.9)."}},"canonical_name":null,"name_confidence":0.66,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Castillos2023_CastillosBurstVEP40","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":4,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}