EEGdash›NeMAR›NM000200

Iss. 200 · 13 subjects · 265 recordings · CC-BY-4.0

Dataset Brief · BigP3BCI Study I — 9x8 checkerboard/performance-based (13 hea…

NM000200: eeg dataset, 13 subjects#

BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)

Citation: Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins (2019). BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects).

13-participant EEG dataset — BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects).

Data & curation Boyla Mainsah · Chance Fleeting · Thomas Balmat · Eric Sellers · Leslie Collins
Year 2019 · Distributed via NeMAR

EEG · 16 ch256 HzBIDS 1.9.0Task · p300HealthyVisualAttention

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 NM000200

dataset = NM000200(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 = NM000200(cache_dir="./data", subject="01")

Advanced query

dataset = NM000200(
    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{nm000200,
  title = {BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)},
  author = {Boyla Mainsah and Chance Fleeting and Thomas Balmat and Eric Sellers and Leslie Collins},
}

§ 02Study · The README

About This Dataset#

BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects).

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)

Target

├─ Sensory-event
├─ Experimental-stimulus

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=1, range 21–21 yr, mean 21.0 yr)

20

Male · 1

Sex composition

Female
6
Male
7
F : M ratio
0.86 : 1

46% female · n = 13 subjects with reported sex.

Channel counts: 16 ch (n=265 recordings)

Sampling frequencies: 256.0 Hz (n=265 recordings)

Total recording duration: 7 h 24 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 256 Hz · 13 subjects, 265 recordings

Live trace viewer — sub-13 · ses-0 · task-p300 · run-0

Showing one representative recording out of 13 subjects and 265 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 · 16 sensors — 16 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.NM000200(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Mainsah2025_BigP3BCI_I

Canonical—

Importable asNM000200 · Mainsah2025_BigP3BCI_I

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

class eegdash.dataset.NM000200(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)

Study:: nm000200 (NeMAR)
Author (year):: Mainsah2025_BigP3BCI_I
Canonical:: —

Also importable as: NM000200, Mainsah2025_BigP3BCI_I.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 265; 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/nm000200 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000200

Examples

>>> from eegdash.dataset import NM000200
>>> dataset = NM000200(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 — NM000200.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 nm000200 to reproduce the tutorial on this dataset.

Citation

Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins (2019). BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects).

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000301EEG · 17 subjSame authors NM000277EEG · 20 subjSame authors NM000218EEG · 16 subjSame authors NM000326EEG · 19 subjSame authors NM000247EEG · 10 subjSame authors

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000200`
Title	BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)
Author (year)	`Mainsah2025_BigP3BCI_I`
Canonical	—
Importable as	`NM000200`, `Mainsah2025_BigP3BCI_I`
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

NM000200: eeg dataset, 13 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#