BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)

BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects).

Dataset Overview

• Code: Mainsah2025-K
• Paradigm: p300
• DOI: 10.13026/0byy-ry86
• Subjects: 5
• Sessions per subject: 2
• Events: Target=2, NonTarget=1
• Trial interval: [0, 1.0] s

Acquisition

• Sampling rate: 256.0 Hz
• Number of channels: 16
• Channel types: eeg=16
• Montage: standard_1020
• Hardware: g.USBamp (g.tec)
• Line frequency: 60.0 Hz

Participants

• Number of subjects: 5
• Health status: healthy

Experimental Protocol

• Paradigm: p300
• Number of classes: 2
• Class labels: Target, NonTarget

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

Signal Processing

• Feature extraction: P300_ERP_detection

Cross-Validation

• Method: calibration-then-test
• Evaluation type: within_subject

BCI Application

• Applications: speller
• Environment: laboratory
• Online feedback: True

Tags

• Modality: visual
• Type: perception

Documentation

• Description: BigP3BCI: the largest public P300 BCI dataset, containing EEG recordings from ~267 subjects across 20 studies using 6x6 or 9x8 character grids with various stimulus paradigms.
• DOI: 10.13026/0byy-ry86
• License: CC-BY-4.0
• Investigators: Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins
• Institution: Duke University; East Tennessee State University
• Country: US
• Repository: PhysioNet
• Data URL: https://physionet.org/content/bigp3bci/1.0.0/
• Publication year: 2025

References

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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