EEGdash›NeMAR›ON002181

Iss. 2181 · 226 subjects · 226 recordings · CC0

Dataset Brief · CRYPTO and PROVIDE EEG Baseline Data

ON002181: eeg dataset, 226 subjects#

CRYPTO and PROVIDE EEG Baseline Data

Citation: Wanze Xie, Sarah Jensen, Mark Wade, Swapna Kumar, Alissa Westerlund, Shahria Kakon, Rashidul Haque, William A Petri, Charles A Nelson (20). CRYPTO and PROVIDE EEG Baseline Data. 10.82901/nemar.on002181

226-participant EEG dataset — CRYPTO and PROVIDE EEG Baseline Data.

Data & curation Wanze Xie · Sarah Jensen · Mark Wade · Swapna Kumar · Alissa Westerlund · Shahria Kakon · …
Year 20 · Distributed via NeMAR

EEG · 125 ch500 HzBIDS 1.2Task · Baseline

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 ON002181

dataset = ON002181(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 = ON002181(cache_dir="./data", subject="01")

Advanced query

dataset = ON002181(
    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{on002181,
  title = {CRYPTO and PROVIDE EEG Baseline Data},
  author = {Wanze Xie and Sarah Jensen and Mark Wade and Swapna Kumar and Alissa Westerlund and Shahria Kakon and Rashidul Haque and William A Petri and Charles A Nelson},
  doi = {10.82901/nemar.on002181},
  url = {https://doi.org/10.82901/nemar.on002181},
}

§ 02Study · The README

About This Dataset#

These are the EEG baseline data used in the study on the association between stunting and EEG brain functional connectivity in Bangladeshi children (https://doi.org/10.1101/447722).

Data with an ID < 2000 were collected for a cohort of 36-month-old toddlers, and those with an ID > 2000 were collected for a cohort of 6-month-old infants. The children were watching screen savers for 2 minutes.

DOI

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=226, range 6–36 yr, mean 22.3 yr)

535

Other · 226

Channel counts: 125 ch (n=226 recordings)

Sampling frequencies: 500.0 Hz (n=226 recordings)

Total recording duration: 7 h 40 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 125 ch · EEG · 500 Hz · 226 subjects, 226 recordings

Live trace viewer — sub-1473 · task-Baseline

Showing one representative recording out of 226 subjects and 226 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 · 124 sensors — 124 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.ON002181(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON002181

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

class eegdash.dataset.ON002181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

CRYPTO and PROVIDE EEG Baseline Data

Study:: on002181 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON002181, nan.

Modality: eeg. Subjects: 226; recordings: 226; 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/on002181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on002181 DOI: https://doi.org/10.82901/nemar.on002181

Examples

>>> from eegdash.dataset import ON002181
>>> dataset = ON002181(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 — ON002181.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 on002181 to reproduce the tutorial on this dataset.

Citation

Wanze Xie, Sarah Jensen, Mark Wade, Swapna Kumar, Alissa Westerlund, … (20). CRYPTO and PROVIDE EEG Baseline Data. 10.82901/nemar.on002181

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on002181.

Related & sibling datasets

ON007823EEG · 169 subj NM000179EEG · 215 subj NM000112EEG · 123 subj ON005628EEG · 102 subj NM000228EEG · 356 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.2

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on002181

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON002181`
Title	CRYPTO and PROVIDE EEG Baseline Data
Author (year)	—
Canonical	—
Importable as	`ON002181`
Year	20
Authors	Wanze Xie, Sarah Jensen, Mark Wade, Swapna Kumar, Alissa Westerlund, Shahria Kakon, Rashidul Haque, William A Petri, Charles A Nelson
License	CC0
Citation / DOI	10.82901/nemar.on002181
Source links	OpenNeuro \| NeMAR \| Source URL

ON002181: eeg dataset, 226 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#