DS007096: eeg dataset, 292 subjects#

PURSUE N170 Face Perception

Access recordings and metadata through EEGDash.

Citation: Couperus, J.W., Bukach, C.M., Reed,C.L. (2025). PURSUE N170 Face Perception. 10.18112/openneuro.ds007096.v1.0.0

Modality: eeg Subjects: 292 Recordings: 292 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007096

dataset = DS007096(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007096(cache_dir="./data", subject="01")

Advanced query

dataset = DS007096(
    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{ds007096,
  title = {PURSUE N170 Face Perception},
  author = {Couperus, J.W. and Bukach, C.M. and Reed,C.L.},
  doi = {10.18112/openneuro.ds007096.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007096.v1.0.0},
}

About This Dataset#

README

Face Perception Task 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

DS007096

Title

PURSUE N170 Face Perception

Author (year)

Couperus2025_PURSUE_N170_Face

Canonical

Couperus2017

Importable as

DS007096, Couperus2025_PURSUE_N170_Face, Couperus2017

Year

2025

Authors

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

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007096.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007096,
  title = {PURSUE N170 Face Perception},
  author = {Couperus, J.W. and Bukach, C.M. and Reed,C.L.},
  doi = {10.18112/openneuro.ds007096.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007096.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: 292

  • Recordings: 292

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 500.0

  • Duration (hours): 51.75700166666667

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 11.6 GB

  • File count: 292

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007096.v1.0.0

Provenance

API Reference#

Use the DS007096 class to access this dataset programmatically.

class eegdash.dataset.DS007096(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

PURSUE N170 Face Perception

Study:

ds007096 (OpenNeuro)

Author (year):

Couperus2025_PURSUE_N170_Face

Canonical:

Couperus2017

Also importable as: DS007096, Couperus2025_PURSUE_N170_Face, Couperus2017.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 292; recordings: 292; 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/ds007096 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007096 DOI: https://doi.org/10.18112/openneuro.ds007096.v1.0.0

Examples

>>> from eegdash.dataset import DS007096
>>> dataset = DS007096(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#