EEGdash›NeMAR›NM000269

Iss. 269 · 13 subjects · 389 recordings · CC-BY-4.0

Dataset Brief · BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects…

NM000269: eeg dataset, 13 subjects#

BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025)

Access recordings and metadata through EEGDash.

Citation: Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins (20). BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025). 10.82901/nemar.nm000269

Modality: eeg Subjects: 13 Recordings: 389 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

13-participant EEG dataset — BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025).

Data & curation Boyla Mainsah · Chance Fleeting · Thomas Balmat · Eric Sellers · Leslie Collins
Senior author Leslie Collins
Year 20 · Distributed via NeMAR

EEG · 32 ch256 HzBIDS 1.9.0Task · p300

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000269

dataset = NM000269(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000269(cache_dir="./data", subject="01")

Advanced query

dataset = NM000269(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

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

Cite This Dataset

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

BibTeX

@dataset{nm000269,
  title = {BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025)},
  author = {Boyla Mainsah and Chance Fleeting and Thomas Balmat and Eric Sellers and Leslie Collins},
  doi = {10.82901/nemar.nm000269},
  url = {https://doi.org/10.82901/nemar.nm000269},
}

§ 02Study · The README

About This Dataset#

Mainsah2025-A is a derivative P300 brain-computer interface dataset containing EEG recordings from 13 healthy subjects (1 session each) performing a visual speller task using a 6x6 checkerboard stimulus paradigm. The dataset comprises 32-channel EEG data sampled at 256 Hz with binary classification of target and non-target events, annotated using HED 8.4.0 schema. This dataset represents Study A of the BigP3BCI collection (10.13026/0byy-ry86), the largest public P300 BCI dataset, and is processed using the MOABB (Mother of All BCI Benchmarks) framework.

EEG data were acquired using a g.USBamp amplifier (g.tec) with 32 channels arranged in a standard 10-20 montage, sampled at 256 Hz with a 60 Hz line frequency notch. Subjects performed a visual P300 speller task using a 6x6 checkerboard stimulus paradigm with row-column and random presentation modes. Trial intervals were defined from 0 to 1.0 seconds post-stimulus, with binary classification of target and non-target events.

DOI

BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000269-blue)](https://doi.org/10.82901/nemar.nm000269) # BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025) ## Overview Mainsah2025-A is a derivative P300 brain-computer interface dataset containing EEG recordings from 13 healthy subjects (1 session each) performing a visual speller task using a 6x6 checkerboard stimulus paradigm. The dataset comprises 32-channel EEG data sampled at 256 Hz with binary classification of target and non-target events, annotated using HED 8.4.0 schema. This dataset represents Study A of the BigP3BCI collection (10.13026/0byy-ry86), the largest public P300 BCI dataset, and is processed using the MOABB (Mother of All BCI Benchmarks) framework. ## Dataset Summary | Property | Value | |---|—| | Subjects | 13 | | Channels | 32 | | Classes | 2 | | Trial length | 1 s | | Sampling frequency | 256 Hz | | Sessions | 1 | | Paradigm | P300 | ## Data Collection Methods EEG data were acquired using a g.USBamp amplifier (g.tec) with 32 channels arranged in a standard 10-20 montage, sampled at 256 Hz with a 60 Hz line frequency notch. Subjects performed a visual P300 speller task using a 6x6 checkerboard stimulus paradigm with row-column and random presentation modes. Trial intervals were defined from 0 to 1.0 seconds post-stimulus, with binary classification of target and non-target events. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import Mainsah2025_A from moabb.paradigms import P300 paradigm = P300() dataset = Mainsah2025_A() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Mainsah2025_A.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.13026/0byy-ry86](https://doi.org/10.13026/0byy-ry86) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: CC-BY-4.0

Authors:

Boyla Mainsah
Chance Fleeting
Thomas Balmat
Eric Sellers
Leslie Collins

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000269

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=389 recordings)

Sampling frequencies (Hz)

256.0256.0256.0256.0

Total recording duration: 15 h 10 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 256 Hz · 13 subjects, 389 recordings

Live trace viewer — sub-1 · ses-0 · task-p300 · run-0

Showing one representative recording out of 13 subjects and 389 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 32 sensors — 32 channels

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.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000269(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000269

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000269(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025)

Study:: nm000269 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000269.

Modality: eeg; Subject type: Unknown. Subjects: 13; recordings: 389; 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/nm000269 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000269 DOI: https://doi.org/10.82901/nemar.nm000269

Examples

>>> from eegdash.dataset import NM000269
>>> dataset = NM000269(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000269.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000269 to reproduce the tutorial on this dataset.

Citation

Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins (20). BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025). 10.82901/nemar.nm000269

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000269.

Related & sibling datasets

ON003190EEG · 19 subj ON007058EEG · 10 subj ON007720EEG · 5 subj NM000262EEG · 19 subj NM000157EEG · 19 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000269

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000269`
Title	BigP3BCI Study A — P300 BCI EEG dataset (13 healthy subjects, Mainsah et al. 2025)
Author (year)	—
Canonical	—
Importable as	`NM000269`
Year	20
Authors	Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000269
Source links	OpenNeuro \| NeMAR \| Source URL

NM000269: eeg dataset, 13 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#