NM000186: eeg dataset, 8 subjects#

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

Access recordings and metadata through EEGDash.

Citation: Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins (2019). BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects).

Modality: eeg Subjects: 8 Recordings: 88 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000186

dataset = NM000186(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000186(cache_dir="./data", subject="01")

Advanced query

dataset = NM000186(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000186,
  title = {BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)},
  author = {Boyla Mainsah and Chance Fleeting and Thomas Balmat and Eric Sellers and Leslie Collins},
}

About This Dataset#

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

View full README

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

Dataset Information#

Dataset ID

NM000186

Title

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

Author (year)

Mainsah2025_BigP3BCI_E

Canonical

BigP3BCI_StudyE, BigP3BCI_E

Importable as

NM000186, Mainsah2025_BigP3BCI_E, BigP3BCI_StudyE, BigP3BCI_E

Year

2019

Authors

Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins

License

CC-BY-4.0

Citation / DOI

Unknown

Source links

OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 8

  • Recordings: 88

  • Tasks: 1

Channels & sampling rate

  • Channels: 16

  • Sampling rate (Hz): 256.0

  • Duration (hours): 2.3882378472222223

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 104.7 MB

  • File count: 88

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000186 class to access this dataset programmatically.

class eegdash.dataset.NM000186(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

Study:

nm000186 (NeMAR)

Author (year):

Mainsah2025_BigP3BCI_E

Canonical:

BigP3BCI_StudyE, BigP3BCI_E

Also importable as: NM000186, Mainsah2025_BigP3BCI_E, BigP3BCI_StudyE, BigP3BCI_E.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 8; recordings: 88; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000186 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000186

Examples

>>> from eegdash.dataset import NM000186
>>> dataset = NM000186(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#