EEGdash›OpenNeuro›DS007521

Iss. 7521 · 23 subjects · 46 recordings · CC0

Dataset Brief · The effect of hunger and state preferences on the neural proc…

DS007521: eeg dataset, 23 subjects#

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

Citation: Moerel, Denise, Chenh, Cecilia, Bowman, Sophie, Carlson, Thomas (20). The effect of hunger and state preferences on the neural processing of food images. 10.18112/openneuro.ds007521.v1.0.1

23-participant EEG dataset — The effect of hunger and state preferences on the neural processing of food images.

Data & curation Moerel, Denise · Chenh, Cecilia · Bowman, Sophie · Carlson, Thomas
Year 20 · Distributed via OpenNeuro
Funding ARC DP200101787 (TAC)

EEG · 64 ch100 HzBIDS 1.0.2Task · food2 sessionsHealthyVisualAttention

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 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},
}

§ 02Study · The README

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=23, range 18–62 yr, mean 22.8 yr)

152060

Other · 23

Sex composition

Female
14
Male
9
F : M ratio
1.56 : 1

61% female · n = 23 subjects with reported sex.

HandednessRight · 20Left · 3

Channel counts: 64 ch (n=46 recordings)

Sampling frequencies: 100.0 Hz (n=46 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 100 Hz · 23 subjects, 46 recordings

Live trace viewer — sub-13 · ses-2 · task-food

Showing one representative recording out of 23 subjects and 46 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.DS007521(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Moerel2026

Canonical—

Importable asDS007521 · Moerel2026

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

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

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)

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 — DS007521.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 ds007521 to reproduce the tutorial on this dataset.

Citation

Moerel, Denise, Chenh, Cecilia, Bowman, Sophie, Carlson, Thomas (20). The effect of hunger and state preferences on the neural processing of food images. 10.18112/openneuro.ds007521.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007521.v1.0.1.

Related & sibling datasets

DS006593EEG · 21 subj DS004841EEG · 20 subj DS004843EEG · 14 subj DS004519EEG · 40 subj DS006563EEG · 12 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events

Provenance

CC0 · 10.18112/openneuro.ds007521.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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	20
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

DS007521: eeg dataset, 23 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#