NM000157: eeg dataset, 19 subjects#

Mainsah2025-B

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 19 Recordings: 544 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000157

dataset = NM000157(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000157(cache_dir="./data", subject="01")

Advanced query

dataset = NM000157(
    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{nm000157,
  title = {Mainsah2025-B},
  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-B

BigP3BCI Study B — 6x6 checkerboard, multi-session (19 healthy subjects).

Dataset Overview

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

View full README

Mainsah2025-B

BigP3BCI Study B — 6x6 checkerboard, multi-session (19 healthy subjects).

Dataset Overview

Code: Mainsah2025-B Paradigm: p300 DOI: 10.13026/0byy-ry86 Subjects: 19 Sessions per subject: 8 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: 19 Health status: patients Clinical population: ALS

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) NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000157

Title

Mainsah2025-B

Author (year)

Mainsah2025

Canonical

—

Importable as

NM000157, Mainsah2025

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{nm000157,
  title = {Mainsah2025-B},
  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: 19

  • Recordings: 544

  • Tasks: 1

Channels & sampling rate

  • Channels: 16

  • Sampling rate (Hz): 256.00008099666394 (205), 256.00004960640416 (161), 256.00006152923214 (98), 256.0 (42), 256.0000879536484 (15), 256.00010844590247 (10), 256.00004522469607 (3), 256.0001184842897 (2), 256.00012796007644 (2), 256.00007397509813 (2), 256.00011633064076 (2), 256.00008239793436

  • Duration (hours): 28.657737184336803

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.2 GB

  • File count: 544

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: doi:10.13026/0byy-ry86

Provenance

Electrode Layout#

Electrode layout — EEG · 16 sensors — 16 channels

Dataset Statistics#

Channel counts: 16 ch (n=543 recordings)

Sampling frequencies (Hz)

256256.0256.0256.0256.0256.0256.0256.0256.0256.0256.0256.0

Total recording duration: 28 h

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

NEMAR pipeline statistics — NM000157
HED event descriptors word cloud HED event descriptors word cloud — NM000157

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Files: —
Size: —
Subjects: —
Click to load file structure…

API Reference#

Use the NM000157 class to access this dataset programmatically.

class eegdash.dataset.NM000157(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Mainsah2025-B

Study:

nm000157 (NeMAR)

Author (year):

Mainsah2025

Canonical:

—

Also importable as: NM000157, Mainsah2025.

Modality: eeg. Subjects: 19; recordings: 544; 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/nm000157 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000157 DOI: https://doi.org/10.13026/0byy-ry86

Examples

>>> from eegdash.dataset import NM000157
>>> dataset = NM000157(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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

See Also#