DS003710#

APPLESEED Example Dataset

Access recordings and metadata through EEGDash.

Citation: Cabell L. Williams, Meghan H. Puglia (2021). APPLESEED Example Dataset. 10.18112/openneuro.ds003710.v1.0.2

Modality: eeg Subjects: 13 Recordings: 293 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003710

dataset = DS003710(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003710(cache_dir="./data", subject="01")

Advanced query

dataset = DS003710(
    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{ds003710,
  title = {APPLESEED Example Dataset},
  author = {Cabell L. Williams and Meghan H. Puglia},
  doi = {10.18112/openneuro.ds003710.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003710.v1.0.2},
}

About This Dataset#

The APPLESEED Example Dataset

This dataset consists of longitudinal EEG recordings from 13 infants at 4, 8, and 12 months of age. Test-retest reliability was assessed at 4 months of age via two appointments (session 1 & 2) that occurred within 1 week of each other. Session 3 data was recorded at 8 months of age and session 4 data was recorded at 12 months of age. Two participants did not return for longitudinal testing at sessions 3 & 4. Therefore, the complete dataset consists of 48 recording sessions, with reliability and longitudinal data (sessions 1-4) for 11 infants (6 F), and reliability data only (sessions 1 & 2) for an additional 2 infants. A channel location file and bin file for analysis are included in the “code” directory.

This dataset was used to develop and validate the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) and is provided as an example dataset to accompany Puglia, M.H., Slobin, J.S., & Williams, C.L., 2022. The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations. Developmental Cognitive Neuroscience, 101163.

APPLESEED code is available to download from mhpuglia/APPLESEED.

This dataset is part of a larger, ongoing longitudinal study initially described in Puglia, M.H., Krol, K.M., Missana, M., Williams, C.L., Lillard, T.S., Morris, J.P., Connelly, J.J. and Grossmann, T., 2020. Epigenetic tuning of brain signal entropy in emergent human social behavior. BMC medicine, 18(1), pp.1-24. https://doi.org/10.1186/s12916-020-01683-x.

Dataset Information#

Dataset ID

DS003710

Title

APPLESEED Example Dataset

Year

2021

Authors

Cabell L. Williams, Meghan H. Puglia

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003710.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003710,
  title = {APPLESEED Example Dataset},
  author = {Cabell L. Williams and Meghan H. Puglia},
  doi = {10.18112/openneuro.ds003710.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003710.v1.0.2},
}

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: 13

  • Recordings: 293

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 5000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 10.2 GB

  • File count: 293

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003710.v1.0.2

Provenance

API Reference#

Use the DS003710 class to access this dataset programmatically.

class eegdash.dataset.DS003710(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003710. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 13; recordings: 48; 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/ds003710 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003710

Examples

>>> from eegdash.dataset import DS003710
>>> dataset = DS003710(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#