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nm000186 NEMAR-native dataset

BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)

Download zip Issues GitHub

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

  1. NeuroScience Gateway (NSG) portal.

    NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000186), and submit.
    nsgportal.org →

  2. Local processing with nemar-cli.

    Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning. 

    npm install -g nemar-cli
nemar dataset clone nm000186
cd nm000186 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000186/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.
DOI 10.82901/nemar.nm000186

![DOI](https://doi.org/10.82901/nemar.nm000186)

BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)

BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects).

Dataset Overview

  • Code: Mainsah2025-E
  • Paradigm: p300
  • DOI: 10.13026/0byy-ry86
  • Subjects: 8
  • Sessions per subject: 1
  • 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: 8
  • 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: P300ERPdetection

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

Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Files

13 top-level entries · 104 MB total