EEGdash›OpenNeuro›DS007137

Iss. 7137 · 294 subjects · 294 recordings · CC0

Dataset Brief · PURSUE N2pc Visual Search

DS007137: eeg dataset, 294 subjects#

PURSUE N2pc Visual Search

Citation: Couperus, J.W., Bukach, C.M., Reed, C.L. (2021). PURSUE N2pc Visual Search. 10.18112/openneuro.ds007137.v1.0.0

294-participant EEG dataset — PURSUE N2pc Visual Search.

Data & curation Couperus, J.W. · Bukach, C.M. · Reed, C.L.
Year 2021 · Distributed via OpenNeuro

EEG · 32 ch500 HzBIDS 1.8.0Task · VisualSearchHealthyVisualAttention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

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” }

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=293, range 18–33 yr, mean 19.6 yr · sex per subject not reported)

15202530

Sex composition

Other
293

Channel counts: 32 ch (n=294 recordings)

Sampling frequencies: 500.0 Hz (n=294 recordings)

Total recording duration: 54 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 500 Hz · 294 subjects, 294 recordings

Live trace viewer — sub-2005 · task-VisualSearch

Showing one representative recording out of 294 subjects and 294 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

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

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS007137(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Couperus2025_N2PC

Canonical—

Importable asDS007137 · Couperus2025_N2PC

Sourceeegdash/dataset/registry.py · [source ↗]

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

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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007137.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds007137 to reproduce the tutorial on this dataset.

Citation

Couperus, J.W., Bukach, C.M., Reed, C.L. (2021). PURSUE N2pc Visual Search. 10.18112/openneuro.ds007137.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds007137.v1.0.0.

Related & sibling datasets

DS007139EEG · 292 subjSame authors DS007056EEG · 286 subjSame authors DS004118EEG · 156 subj DS007172EEG · 100 subj DS005866EEG · 60 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007137.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007137`
Title	PURSUE N2pc Visual Search
Author (year)	`Couperus2025_N2PC`
Canonical	—
Importable as	`DS007137`, `Couperus2025_N2PC`
Year	2021
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

DS007137: eeg dataset, 294 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#