DS006040: eeg dataset, 28 subjects#

Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI

Access recordings and metadata through EEGDash.

Citation: Younghwa Cha, Yeji Lee, Eunhee Ji, SoHyun Han, Sunhyun Min, Hyoungkyu Kim, Minseo Cho, Hae Seong Lee, Youngjai Park, Joon-Young Moon (2025). Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI. 10.18112/openneuro.ds006040.v1.0.2

Modality: eeg Subjects: 28 Recordings: 392 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006040

dataset = DS006040(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 = DS006040(cache_dir="./data", subject="01")

Advanced query

dataset = DS006040(
    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{ds006040,
  title = {Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI},
  author = {Younghwa Cha and Yeji Lee and Eunhee Ji and SoHyun Han and Sunhyun Min and Hyoungkyu Kim and Minseo Cho and Hae Seong Lee and Youngjai Park and Joon-Young Moon},
  doi = {10.18112/openneuro.ds006040.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds006040.v1.0.2},
}

About This Dataset#

This dataset includes simultaneous recordings of electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion-weighted imaging (DWI) from 28 participants aged 19 to 42 years. The fMRI and DWI data were acquired using a 3T MRI scanner (Siemens Magnetom Prisma), and the EEG was recorded using 64 channels (Brain Product BrainCap MR with Multirodes). The following tasks were performed: resting state (eyes open and closed), checkerboard (15Hz), gradCPT, and imagery task. Raw files can be found in the subfolders, while preprocessed files are available in the derivatives folder. For more detailed information about the file structure, please refer to the readme files.

Dataset Information#

Dataset ID	`DS006040`
Title	Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI
Author (year)	`Cha2025`
Canonical	—
Importable as	`DS006040`, `Cha2025`
Year	2025
Authors	Younghwa Cha, Yeji Lee, Eunhee Ji, SoHyun Han, Sunhyun Min, Hyoungkyu Kim, Minseo Cho, Hae Seong Lee, Youngjai Park, Joon-Young Moon
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006040.v1.0.2
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: 28
Recordings: 392
Tasks: 10

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 5000.0
Duration (hours): Not calculated

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#

Channel counts: 64 ch (n=391 recordings)

Sampling frequencies: 5000.0 Hz (n=391 recordings)

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 DS006040 class to access this dataset programmatically.

class eegdash.dataset.DS006040(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI

Study:: ds006040 (OpenNeuro)
Author (year):: Cha2025
Canonical:: —

Also importable as: DS006040, Cha2025.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 28; recordings: 392; tasks: 10.

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/ds006040 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006040 DOI: https://doi.org/10.18112/openneuro.ds006040.v1.0.2

Examples

>>> from eegdash.dataset import DS006040
>>> dataset = DS006040(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)