NM000313: eeg dataset, 24 subjects#

Mainsah2025-S2

Access recordings and metadata through EEGDash.

Citation: Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins (2019). Mainsah2025-S2. 10.13026/0byy-ry86

Modality: eeg Subjects: 24 Recordings: 288 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000313

dataset = NM000313(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000313(cache_dir="./data", subject="01")

Advanced query

dataset = NM000313(
    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{nm000313,
  title = {Mainsah2025-S2},
  author = {Boyla Mainsah and Chance Fleeting and Thomas Balmat and Eric Sellers and Leslie Collins},
  doi = {10.13026/0byy-ry86},
  url = {https://doi.org/10.13026/0byy-ry86},
}

About This Dataset#

Mainsah2025-S2

BigP3BCI Study S2 — 9x8 house/tool paradigm (24 healthy subjects).

Dataset Overview

Code: Mainsah2025-S2 Paradigm: p300 DOI: 10.13026/0byy-ry86

View full README

Mainsah2025-S2

BigP3BCI Study S2 — 9x8 house/tool paradigm (24 healthy subjects).

Dataset Overview

Code: Mainsah2025-S2 Paradigm: p300 DOI: 10.13026/0byy-ry86 Subjects: 24 Sessions per subject: 1 Events: Target=2, NonTarget=1 Trial interval: [0, 1.0] s

Acquisition

Sampling rate: 256.0 Hz Number of channels: 32 Channel types: eeg=32 Montage: standard_1020 Hardware: g.USBamp (g.tec) Line frequency: 60.0 Hz

Participants

Number of subjects: 24 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

NM000313

Title

Mainsah2025-S2

Author (year)

Mainsah2025_S2

Canonical

—

Importable as

NM000313, Mainsah2025_S2

Year

2019

Authors

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

License

CC-BY-4.0

Citation / DOI

doi:10.13026/0byy-ry86

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000313,
  title = {Mainsah2025-S2},
  author = {Boyla Mainsah and Chance Fleeting and Thomas Balmat and Eric Sellers and Leslie Collins},
  doi = {10.13026/0byy-ry86},
  url = {https://doi.org/10.13026/0byy-ry86},
}

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: 24

  • Recordings: 288

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 256.0000766323896

  • Duration (hours): 13.359683500841909

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 1.1 GB

  • File count: 288

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: doi:10.13026/0byy-ry86

Provenance

API Reference#

Use the NM000313 class to access this dataset programmatically.

class eegdash.dataset.NM000313(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Mainsah2025-S2

Study:

nm000313 (NeMAR)

Author (year):

Mainsah2025_S2

Canonical:

—

Also importable as: NM000313, Mainsah2025_S2.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 288; 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/nm000313 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000313 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000313
>>> dataset = NM000313(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#