EEGdash›OpenNeuro›DS004521
Iss. 4521 · 34 subjects · 34 recordings · CC0
Dataset Brief · Changes in behavioral priority influence the accessibility of…

DS004521: eeg dataset, 34 subjects#

Changes in behavioral priority influence the accessibility of working memory content - Experiment 1

Citation: Edward Ester, Paige Pytel (—). Changes in behavioral priority influence the accessibility of working memory content - Experiment 1. 10.18112/openneuro.ds004521.v1.0.1

34-participant EEG dataset — Changes in behavioral priority influence the accessibility of working memory content - Experiment 1.

Data & curation Edward Ester · Paige Pytel
EEG · 62 ch250 HzBIDS 1.8.0Task · PostcuesHealthyMemory
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004521

dataset = DS004521(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004521(cache_dir="./data", subject="01")

Advanced query

dataset = DS004521(
    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{ds004521,
  title = {Changes in behavioral priority influence the accessibility of working memory content - Experiment 1},
  author = {Edward Ester and Paige Pytel},
  doi = {10.18112/openneuro.ds004521.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004521.v1.0.1},
}
§ 02Study · The README

About This Dataset#

Preprocessed data from Experiment 1 of Ester & Pytel “Changes in behavioral priority influence the accessibility of working memory content”. Analytic scripts for this project can be found on OSF: https://osf.io/gtd5f/. Note that to analyze the BIDS data, you’ll need to modify the analysis scripts to read in the BIDS .set files rather than the expected .mat files. See the OSF wiki for more information

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 62 ch (n=34 recordings)

Sampling frequencies: 250.0 Hz (n=34 recordings)

Total recording duration: 50 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 62 ch · EEG · 250 Hz · 34 subjects, 34 recordings
Live trace viewer — sub-021 · task-Postcues

Showing one representative recording out of 34 subjects and 34 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 · 62 sensors — 62 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.

NEMAR pipeline statistics — DS004521
HED event descriptors word cloud HED event descriptors word cloud — DS004521
§ 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…
Full dataset metadata table

Dataset ID

DS004521

Title

Changes in behavioral priority influence the accessibility of working memory content - Experiment 1

Author (year)

Ester2023_Changes_behavioral

Canonical

—

Importable as

DS004521, Ester2023_Changes_behavioral

Year

—

Authors

Edward Ester, Paige Pytel

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004521.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004521,
  title = {Changes in behavioral priority influence the accessibility of working memory content - Experiment 1},
  author = {Edward Ester and Paige Pytel},
  doi = {10.18112/openneuro.ds004521.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004521.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS004521(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Ester2023_Changes_behavioral
Canonical—
Importable asDS004521 · Ester2023_Changes_behavioral
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS004521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Changes in behavioral priority influence the accessibility of working memory content - Experiment 1

Study:

ds004521 (OpenNeuro)

Author (year):

Ester2023_Changes_behavioral

Canonical:

—

Also importable as: DS004521, Ester2023_Changes_behavioral.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 34; recordings: 34; 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/ds004521 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004521 DOI: https://doi.org/10.18112/openneuro.ds004521.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS004521
>>> dataset = DS004521(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/ds004521 · pull with datasets.load_dataset("EEGDash/ds004521").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS004521.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 ds004521 to reproduce the tutorial on this dataset.

Citation

Edward Ester, Paige Pytel (n.d.). Changes in behavioral priority influence the accessibility of working memory content - Experiment 1. 10.18112/openneuro.ds004521.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004521.v1.0.1.

BIDS
BIDS 1.8.0
Sidecars
events · channels · electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#