EEGdash›OpenNeuro›DS004151

Iss. 4151 · 57 subjects · 57 recordings · CC0

Dataset Brief · Effect of obesity on inhibitory control in preadolescents dur…

DS004151: eeg dataset, 57 subjects#

Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study

Citation: Graciela C. Alatorre-Cruz, Heather Downs, Darcy Hagood, Seth T. Sorensen, D. Keith Williams, Linda Larson-Prior (20). Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study. 10.18112/openneuro.ds004151.v1.0.0

57-participant EEG dataset — Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study.

Data & curation Graciela C. Alatorre-Cruz · Heather Downs · Darcy Hagood · Seth T. Sorensen · D. Keith Williams · Linda Larson-Prior
Year 20 · Distributed via OpenNeuro
Funding This research was supported by USDA/Agricultural Research Service Project 6026-51000-012-06S

EEG · 128 ch500 HzBIDS 1Task · stopObeseVisualAttention

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 DS004151

dataset = DS004151(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 = DS004151(cache_dir="./data", subject="01")

Advanced query

dataset = DS004151(
    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{ds004151,
  title = {Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study},
  author = {Graciela C. Alatorre-Cruz and Heather Downs and Darcy Hagood and Seth T. Sorensen and D. Keith Williams and Linda Larson-Prior},
  doi = {10.18112/openneuro.ds004151.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004151.v1.0.0},
}

§ 02Study · The README

About This Dataset#

Introduction

This EEG dataset contains the electrophysiological signal from fifty-seven obese and non-obese preteens during a stop-signal task. The stimuli were designed and administered using E-Prime software (Version 2) at Arkansas Children Nutrition Center (ACNC), Little Rock, Arkansas.

The University of Arkansas for Medical Sciences (UAMS) approved the study protocol. This research

was supported by USDA/Agricultural Research Service Project 6026-51000-012-06S.

Raw data files The data were acquired with a Geodesic Net Amps 300 system running Netstation 4.5.2 software using the 128-channel Geodesic Hydrocell Sensor Net™ (Magstim EGI., Eugene OR, USA).

No operations have been performed on the data. Participant data The Participants.tsv file contains age, gender, body mass index (BMI), and weight status (WS) How to cite All use of this dataset in a publication context requires the following paper to be cited: Alatorre-Cruz GC, Downs H, Hagood D, Sorensen ST, Williams DK, Larson-Prior L. Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study. Int J Psychophysiol. 2021 Jul;165:56-67. doi: 10.1016/j.ijpsycho.2021.04.003 Contact Questions regarding the EEG data may be addressed to Catalina Alatorre-Cruz (gcalatorrecruz@uams.edu).

Question regarding the project, in general, may be addressed to Linda Larson-Prior (ljlarsonprior@uams.edu).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=57, range 9–10 yr, mean 9.6 yr · sex per subject not reported)

510

Sex composition

Female
36
Male
21
F : M ratio
1.71 : 1

63% female · n = 57 subjects with reported sex.

Channel counts: 128 ch (n=57 recordings)

Sampling frequencies: 500.0 Hz (n=57 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 500 Hz · 57 subjects, 57 recordings

Live trace viewer — sub-13 · task-stop

Showing one representative recording out of 57 subjects and 57 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 · 128 sensors — 128 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.DS004151(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)AlatorreCruz2022_Effect_obesity

Canonical—

Importable asDS004151 · AlatorreCruz2022_Effect_obesity

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

class eegdash.dataset.DS004151(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study

Study:: ds004151 (OpenNeuro)
Author (year):: AlatorreCruz2022_Effect_obesity
Canonical:: —

Also importable as: DS004151, AlatorreCruz2022_Effect_obesity.

Modality: eeg; Experiment type: Attention; Subject type: Obese. Subjects: 57; recordings: 57; 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/ds004151 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004151 DOI: https://doi.org/10.18112/openneuro.ds004151.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004151
>>> dataset = DS004151(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 FacePre-bundled mirror at EEGDash/ds004151 · pull with datasets.load_dataset("EEGDash/ds004151").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004151.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 ds004151 to reproduce the tutorial on this dataset.

Citation

Graciela C. Alatorre-Cruz, Heather Downs, Darcy Hagood, Seth T. Sorensen, D. Keith Williams, … (20). Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study. 10.18112/openneuro.ds004151.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004151.v1.0.0.

Related & sibling datasets

DS005866EEG · 60 subj DS005868EEG · 48 subj DS004519EEG · 40 subj DS005960EEG · 41 subj DS006171EEG · 36 subj

+ 1 more — see See Also below →

BIDS

BIDS 1

Sidecars

events · events.json · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004151.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004151`
Title	Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study
Author (year)	`AlatorreCruz2022_Effect_obesity`
Canonical	—
Importable as	`DS004151`, `AlatorreCruz2022_Effect_obesity`
Year	20
Authors	Graciela C. Alatorre-Cruz, Heather Downs, Darcy Hagood, Seth T. Sorensen, D. Keith Williams, Linda Larson-Prior
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004151.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS004151: eeg dataset, 57 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#