RSVP ERP dataset for authentication from Zhang et al 2025

RSVP ERP dataset for authentication from Zhang et al 2025.

Dataset Overview

• Code: Zhang2025
• Paradigm: p300
• DOI: 10.1038/s41597-025-05378-x
• Subjects: 15
• Sessions per subject: 4
• Events: Target=2, NonTarget=1
• Trial interval: [0, 0.6] s
• Runs per session: 4
• File format: MATLAB (HDF5)

Acquisition

• Sampling rate: 1000.0 Hz
• Number of channels: 57
• Channel types: eeg=57
• Channel names: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO7, PO8, Oz, O1, O2
• Montage: standard_1020
• Hardware: Neuracle Neusen
• Reference: CPz
• Ground: AFz
• Line frequency: 50.0 Hz

Participants

• Number of subjects: 15
• Health status: healthy
• Age: min=22, max=26
• Gender distribution: female=6, male=9
• Handedness: all right-handed
• Species: human

Experimental Protocol

• Paradigm: p300
• Number of classes: 2
• Class labels: Target, NonTarget
• Trial duration: 1.0 s
• Study design: RSVP face authentication; self-face vs AI-generated faces; 4 sessions over 200 days (longitudinal)
• Feedback type: none
• Stimulus type: RSVP face images
• Stimulus modalities: visual
• Primary modality: visual
• Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

  Target
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Target

  NonTarget
    ├─ Sensory-event
    ├─ Experimental-stimulus
    ├─ Visual-presentation
    └─ Non-target

Paradigm-Specific Parameters

• Detected paradigm: p300
• Stimulus onset asynchrony: 100.0 ms

Data Structure

• Trials: ~160 target + ~6240 nontarget per session
• Trials context: per session (4 blocks x 8 sequences x 200 images)

Signal Processing

• Classifiers: HDCA
• Feature extraction: HDCA
• Frequency bands: ERP_dominant=[0.0, 10.0] Hz

Cross-Validation

• Evaluation type: within_subject

BCI Application

• Applications: identity_authentication, target_detection
• Environment: laboratory

Tags

• Pathology: Healthy
• Modality: ERP
• Type: RSVP

Documentation

• DOI: 10.1038/s41597-025-05378-x
• License: CC-BY-NC-ND-4.0
• Investigators: Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang, Xiaorong Gao, Chen Yang
• Institution: Beijing University of Posts and Telecommunications
• Country: CN
• Data URL: https://figshare.com/articles/dataset/27201003
• Publication year: 2025

References

Zhang, Y., Zhang, H., Li, Y., Wang, Y., Gao, X., & Yang, C. (2025). A longitudinal EEG dataset of event-related potential for EEG-based identity authentication. Scientific Data, 12, 1069. https://doi.org/10.1038/s41597-025-05378-x Appelhoff, 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

Pernet, 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

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