DS002181#

CRYPTO and PROVIDE EEG Baseline Data

Access recordings and metadata through EEGDash.

Citation: Wanze Xie, Sarah Jensen, Mark Wade, Swapna Kumar, Alissa Westerlund, Shahria Kakon, Rashidul Haque, William A Petri, Charles A Nelson (2019). CRYPTO and PROVIDE EEG Baseline Data. mockDOI

Modality: eeg Subjects: 226 Recordings: 1134 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002181

dataset = DS002181(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002181(cache_dir="./data", subject="01")

Advanced query

dataset = DS002181(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

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

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

BibTeX

@dataset{ds002181,
  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 = {mockDOI},
  url = {https://doi.org/mockDOI},
}

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.

Dataset Information#

Dataset ID

DS002181

Title

CRYPTO and PROVIDE EEG Baseline Data

Year

2019

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

mockDOI

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002181,
  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 = {mockDOI},
  url = {https://doi.org/mockDOI},
}

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: 226

  • Recordings: 1134

  • Tasks: 1

Channels & sampling rate

  • Channels: 125 (226), 124 (226)

  • Sampling rate (Hz): 500.0

  • Duration (hours): 0.0

Tags

  • Pathology: Development

  • Modality: Visual

  • Type: Resting-state

Files & format

  • Size on disk: 150.9 MB

  • File count: 1134

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: mockDOI

Provenance

API Reference#

Use the DS002181 class to access this dataset programmatically.

class eegdash.dataset.DS002181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002181. Modality: eeg; Experiment type: Resting-state; Subject type: Development. 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/ds002181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002181

Examples

>>> from eegdash.dataset import DS002181
>>> dataset = DS002181(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

See Also#