DS004519#

Internal selective attention is delayed by competition between endogenous and exogenous factors

Access recordings and metadata through EEGDash.

Citation: Edward Ester, Asal Nouri (2023). Internal selective attention is delayed by competition between endogenous and exogenous factors. 10.18112/openneuro.ds004519.v1.0.1

Modality: eeg Subjects: 40 Recordings: 286 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004519

dataset = DS004519(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 = DS004519(cache_dir="./data", subject="01")

Advanced query

dataset = DS004519(
    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{ds004519,
  title = {Internal selective attention is delayed by competition between endogenous and exogenous factors},
  author = {Edward Ester and Asal Nouri},
  doi = {10.18112/openneuro.ds004519.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004519.v1.0.1},
}

About This Dataset#

Preprocessed data files from “Internal selective attention is delayed by competition between endogenous and exogenous factors”. A preprint describing the work can be found at https://www.biorxiv.org/content/10.1101/2022.07.05.498906v4.abstract, and analysis scripts can be found at https://osf.io/wat6d/. This study was conceptualized and analyzed before our lab made the switch to BIDS archival. If you want to use the analysis scripts linked above to analyze the BIDS data, you’ll have to modify them to load the BIDS .set files rather than the .mat files we analyzed in our lab (the .set and .mat files, however, are identical). You will also need to modify the analysis scripts to load in the _behavSummary.mat files for alignment with the EEG data. If you have questions or run into problems, please e-mail the corresponding author of the study (eester@unr.edu)”

Dataset Information#

Dataset ID	`DS004519`
Title	Internal selective attention is delayed by competition between endogenous and exogenous factors
Year	2023
Authors	Edward Ester, Asal Nouri
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004519.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: 40
Recordings: 286
Tasks: 1

Channels & sampling rate

Channels: 62
Sampling rate (Hz): 250.0
Duration (hours): 0.0

API Reference#

Use the DS004519 class to access this dataset programmatically.

class eegdash.dataset.DS004519(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004519. Modality: eeg; Experiment type: Attention. Subjects: 40; recordings: 40; 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/ds004519 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004519

Examples

>>> from eegdash.dataset import DS004519
>>> dataset = DS004519(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