DS004521: eeg dataset, 34 subjects#

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

Access recordings and metadata through EEGDash.

Citation: Edward Ester, Paige Pytel (2023). Changes in behavioral priority influence the accessibility of working memory content - Experiment 1. 10.18112/openneuro.ds004521.v1.0.1

Modality: eeg Subjects: 34 Recordings: 34 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

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

Dataset Information#

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	2023
Authors	Edward Ester, Paige Pytel
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004521.v1.0.1
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: 34
Recordings: 34
Tasks: 1

Channels & sampling rate

Channels: 62
Sampling rate (Hz): 250.0
Duration (hours): 0.0566666666666666

Electrode Layout#

Electrode layout — EEG · 62 sensors — 62 channels

Dataset Statistics#

Channel counts: 62 ch (n=34 recordings)

Sampling frequencies: 250.0 Hz (n=34 recordings)

Total recording duration: 3 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS004521 class to access this dataset programmatically.

class eegdash.dataset.DS004521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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)