DS003710: eeg dataset, 13 subjects#
APPLESEED Example Dataset
Citation: Cabell L. Williams, Meghan H. Puglia (20). APPLESEED Example Dataset. 10.18112/openneuro.ds003710.v1.0.2
13-participant EEG dataset — APPLESEED Example Dataset.
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.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 32 ch (n=48 recordings)
Sampling frequencies: 5000.0 Hz (n=48 recordings)
Total recording duration: 9 h 9 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-4 · task-appleseedexample
Showing one representative recording out of
13 subjects and 48 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 · 32 sensors — 32 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
APPLESEED Example Dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Cabell L. Williams, Meghan H. Puglia |
License |
CC0 |
Citation / DOI |
|
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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003710 · Williams2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003710(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
APPLESEED Example Dataset
- Study:
ds003710(OpenNeuro)- Author (year):
Williams2021- Canonical:
—
Also importable as:
DS003710,Williams2021.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds003710.v1.0.2 NEMAR citation count: 1
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: 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003710").huggingface
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 ds003710 to reproduce the tutorial on this dataset.
Citation
Cabell L. Williams, Meghan H. Puglia (20). APPLESEED Example Dataset. 10.18112/openneuro.ds003710.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003710.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset