DS007052: eeg dataset, 288 subjects#

PURSUE N400 Word Processing

Access recordings and metadata through EEGDash.

Citation: Couperus, J.W., Bukach, C.M., Reed, C.L. (2025). PURSUE N400 Word Processing. 10.18112/openneuro.ds007052.v1.1.2

Modality: eeg Subjects: 288 Recordings: 288 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007052

dataset = DS007052(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 = DS007052(cache_dir="./data", subject="01")

Advanced query

dataset = DS007052(
    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{ds007052,
  title = {PURSUE N400 Word Processing},
  author = {Couperus, J.W. and Bukach, C.M. and Reed, C.L.},
  doi = {10.18112/openneuro.ds007052.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds007052.v1.1.2},
}

About This Dataset#

README

Word Processing 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	`DS007052`
Title	PURSUE N400 Word Processing
Author (year)	`Couperus2025_N400`
Canonical	—
Importable as	`DS007052`, `Couperus2025_N400`
Year	2025
Authors	Couperus, J.W., Bukach, C.M., Reed, C.L.
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007052.v1.1.2
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: 288
Recordings: 288
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 500.0
Duration (hours): 40.00969111111111

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=287, range 18–33 yr)

15202530

Sex distribution

287

Other Total: 287

Channel counts: 32 ch (n=288 recordings)

Sampling frequencies: 500.0 Hz (n=288 recordings)

Total recording duration: 40 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 DS007052 class to access this dataset programmatically.

class eegdash.dataset.DS007052(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

PURSUE N400 Word Processing

Study:: ds007052 (OpenNeuro)
Author (year):: Couperus2025_N400
Canonical:: —

Also importable as: DS007052, Couperus2025_N400.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 288; recordings: 288; 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/ds007052 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007052 DOI: https://doi.org/10.18112/openneuro.ds007052.v1.1.2

Examples

>>> from eegdash.dataset import DS007052
>>> dataset = DS007052(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)