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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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	—
Importable as	`DS007137`, `Couperus2025_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

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

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=293, range 18–33 yr)

15202530

Sex distribution

293

Other Total: 293

Channel counts: 32 ch (n=294 recordings)

Sampling frequencies: 500.0 Hz (n=294 recordings)

Total recording duration: 54 h

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 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:: —

Also importable as: DS007137, Couperus2025_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: 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)