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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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	—
Importable as	`DS007521`, `Moerel2026`
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

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): 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#

Age distribution (n=23, range 18–62 yr)

152060

Sex distribution

14

9

Female Male Total: 23

Channel counts: 64 ch (n=46 recordings)

Sampling frequencies: 100.0 Hz (n=46 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 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:: —

Also importable as: DS007521, Moerel2026.

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: 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)