DS005863#

Cognitive Electrophysiology in Socioeconomic Context in Adulthood

Access recordings and metadata through EEGDash.

Citation: Elif Isbell, Amanda N. Peters, Dylan M. Richardson, Nancy E. R. De León (2025). Cognitive Electrophysiology in Socioeconomic Context in Adulthood. 10.18112/openneuro.ds005863.v2.0.0

Modality: eeg Subjects: 127 Recordings: 2147 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005863

dataset = DS005863(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 = DS005863(cache_dir="./data", subject="01")

Advanced query

dataset = DS005863(
    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{ds005863,
  title = {Cognitive Electrophysiology in Socioeconomic Context in Adulthood},
  author = {Elif Isbell and Amanda N. Peters and Dylan M. Richardson and Nancy E. R. De León},
  doi = {10.18112/openneuro.ds005863.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005863.v2.0.0},
}

About This Dataset#

The “Cognitive Electrophysiology in Socioeconomic Context in Adulthood” Dataset

Data Description

This dataset comprises electroencephalogram (EEG) data collected from 127 young adults (18-30 years), along with retrospective objective and subjective indicators of childhood family socioeconomic status (SES), as well as SES indicators in adulthood, such as educational attainment, individual and household income, food security, and home and neighborhood characteristics. The EEG data were recorded with tasks directly acquired from the Event-Related Potentials Compendium of Open Resources and Experiments ERP CORE (Kappenman et al., 2021), or adapted from these tasks (Isbell et al., 2024). These tasks, which are publicly available, were optimized to capture neural activity manifest in perception, cognition, and action, in neurotypical young adults. Furthermore, the dataset includes a symptoms checklist, consisting of questions that were found to be predictive of symptoms consistent with attention-deficit/hyperactivity disorder (ADHD) in adulthood, which can be used to investigate the links between ADHD symptoms and neural activity in a socioeconomically diverse young adult sample.

Notes

Before the data were publicly shared, all identifiable information was removed, including date of birth, race/ethnicity, zip code, and names of the languages the participants reported to speaking and understanding fluently. Date of birth was used to compute age in years, which is included in the dataset. The dataset consists of participants recruited for studies on adult cognition in context. To provide the largest sample size, we included all participants who completed at least one of the EEG tasks of interest. Each participant completed each EEG task only once. The original participant IDs with which the EEG data were saved were recoded and the raw EEG files were renamed to make the dataset BIDS compatible.

Copyright and License

This dataset is licensed under CC0.

References

Isbell, E., De León, N. E. R., & Richardson, D. M. (2024). Childhood family socioeconomic status is linked to adult brain electrophysiology. PloS One, 19(8), e0307406.

Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP CORE: An open resource for human event-related potential research. NeuroImage, 225, 117465.

Dataset Information#

Dataset ID	`DS005863`
Title	Cognitive Electrophysiology in Socioeconomic Context in Adulthood
Year	2025
Authors	Elif Isbell, Amanda N. Peters, Dylan M. Richardson, Nancy E. R. De León
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005863.v2.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

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: 127
Recordings: 2147
Tasks: 4

Channels & sampling rate

Channels: 27
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005863 class to access this dataset programmatically.

class eegdash.dataset.DS005863(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005863. Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 127; recordings: 357; tasks: 4.

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/ds005863 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005863

Examples

>>> from eegdash.dataset import DS005863
>>> dataset = DS005863(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None