DS007521: eeg dataset, 23 subjects#

The effect of hunger and state preferences on the neural processing of food images

Access recordings and metadata through EEGDash.

Citation: Moerel, Denise, Chenh, Cecilia, Bowman, Sophie, Carlson, Thomas (2026). The effect of hunger and state preferences on the neural processing of food images. 10.18112/openneuro.ds007521.v1.0.1

Modality: eeg Subjects: 23 Recordings: 46 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007521

dataset = DS007521(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007521(cache_dir="./data", subject="01")

Advanced query

dataset = DS007521(
    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{ds007521,
  title = {The effect of hunger and state preferences on the neural processing of food images},
  author = {Moerel, Denise and Chenh, Cecilia and Bowman, Sophie and Carlson, Thomas},
  doi = {10.18112/openneuro.ds007521.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007521.v1.0.1},
}

About This Dataset#

A preprint of the manuscript can be found on bioRxiv: doi.org/10.1101/2025.09.09.674354 The experiment and analysis code can be found via the Open Science Framework: doi.org/10.17605/OSF.IO/ZFD7P Experiment Details: Human electroencephalography recordings from 23 participants, who did a letter task and calorie categorisation task. In the letter task, participants viewed rapid streams of overlaid food/non-food images and letters, pressing a button whenever they saw a vowel, while ignoring the images. This setup directed attention away from the visual objects, making them task-irrelevant. In contrast, the calorie categorisation task required participants to actively evaluate each food image and classify it as higher or lower in calories than bread, by pressing a button. Experiment length: 1 hour

Dataset Information#

Dataset ID

DS007521

Title

The effect of hunger and state preferences on the neural processing of food images

Author (year)

Moerel2026

Canonical

Moerel2025

Importable as

DS007521, Moerel2026, Moerel2025

Year

2026

Authors

Moerel, Denise, Chenh, Cecilia, Bowman, Sophie, Carlson, Thomas

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007521.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007521,
  title = {The effect of hunger and state preferences on the neural processing of food images},
  author = {Moerel, Denise and Chenh, Cecilia and Bowman, Sophie and Carlson, Thomas},
  doi = {10.18112/openneuro.ds007521.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007521.v1.0.1},
}

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: 23

  • Recordings: 46

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 100.0

  • Duration (hours): 34.23017222222222

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 29.0 GB

  • File count: 46

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007521.v1.0.1

Provenance

API Reference#

Use the DS007521 class to access this dataset programmatically.

class eegdash.dataset.DS007521(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

The effect of hunger and state preferences on the neural processing of food images

Study:

ds007521 (OpenNeuro)

Author (year):

Moerel2026

Canonical:

Moerel2025

Also importable as: DS007521, Moerel2026, Moerel2025.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 23; recordings: 46; 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/ds007521 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007521 DOI: https://doi.org/10.18112/openneuro.ds007521.v1.0.1

Examples

>>> from eegdash.dataset import DS007521
>>> dataset = DS007521(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#