DS007137: eeg dataset, 294 subjects#

PURSUE N2pc Visual Search

Access recordings and metadata through EEGDash.

Citation: Couperus, J.W., Bukach, C.M., Reed, C.L. (2025). PURSUE N2pc Visual Search. 10.18112/openneuro.ds007137.v1.0.0

Modality: eeg Subjects: 294 Recordings: 294 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007137

dataset = DS007137(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007137(cache_dir="./data", subject="01")

Advanced query

dataset = DS007137(
    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{ds007137,
  title = {PURSUE N2pc Visual Search},
  author = {Couperus, J.W. and Bukach, C.M. and Reed, C.L.},
  doi = {10.18112/openneuro.ds007137.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007137.v1.0.0},
}

About This Dataset#

Visual Search Experiment from the PURSUE project (pursureerp.com). Data collected from participants at 3 different primarily undergraduate academic institutions (Southern California, Massachusetts, and Virginia) in 2017 and 2018. The task design can be found in the publication by Kappenman et al.(2021). ERP CORE: An open resource for human event-related potential research. NeuroImage, 225, 117465. Details of task are found in the supplementary materials. Race Key: “Levels”: { “x1”: “White”, “x2”: “Black/African American”, “x3”: “Native American”, “x4”: “Asian”, “x5”: “Pacific Islander”, “x6”: “Hispanic/Latino”, “x7”: “Other”, “x8”: “Prefer not to respond”, “x9”: “Chose more than one response”, “” : “empty” }

Dataset Information#

Dataset ID

DS007137

Title

PURSUE N2pc Visual Search

Author (year)

Couperus2025_N2PC

Canonical

Couperus2021_N2pc

Importable as

DS007137, Couperus2025_N2PC, Couperus2021_N2pc

Year

2025

Authors

Couperus, J.W., Bukach, C.M., Reed, C.L.

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007137.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007137,
  title = {PURSUE N2pc Visual Search},
  author = {Couperus, J.W. and Bukach, C.M. and Reed, C.L.},
  doi = {10.18112/openneuro.ds007137.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007137.v1.0.0},
}

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: 294

  • Recordings: 294

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 500.0

  • Duration (hours): 54.44088055555556

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 12.2 GB

  • File count: 294

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007137.v1.0.0

Provenance

API Reference#

Use the DS007137 class to access this dataset programmatically.

class eegdash.dataset.DS007137(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

PURSUE N2pc Visual Search

Study:

ds007137 (OpenNeuro)

Author (year):

Couperus2025_N2PC

Canonical:

Couperus2021_N2pc

Also importable as: DS007137, Couperus2025_N2PC, Couperus2021_N2pc.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 294; recordings: 294; 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/ds007137 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007137 DOI: https://doi.org/10.18112/openneuro.ds007137.v1.0.0

Examples

>>> from eegdash.dataset import DS007137
>>> dataset = DS007137(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#