EEGdash›OpenNeuro›DS007012

Iss. 7012 · 117 subjects · 118 recordings · CC0

Dataset Brief · FOODEEG

DS007012: eeg dataset, 117 subjects#

FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

Citation: Chae, Violet, Grootswagers, Tijl, Bode, Stefan, Feuerriegel, Daniel (2025). FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images. 10.18112/openneuro.ds007012.v1.1.0

117-participant EEG dataset — FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images.

Data & curation Chae, Violet · Grootswagers, Tijl · Bode, Stefan · Feuerriegel, Daniel
Year 2025 · Distributed via OpenNeuro
Funding ARC DE220101508 (DF) · ARC DE230100380 (TG)

EEG · 65 ch512 HzBIDS 1.8.0Task · categorisation2 sessions

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 DS007012

dataset = DS007012(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 = DS007012(cache_dir="./data", subject="01")

Advanced query

dataset = DS007012(
    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{ds007012,
  title = {FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images},
  author = {Chae, Violet and Grootswagers, Tijl and Bode, Stefan and Feuerriegel, Daniel},
  doi = {10.18112/openneuro.ds007012.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds007012.v1.1.0},
}

§ 02Study · The README

About This Dataset#

The FOODEEG dataset comprises of human electroencephalographic (EEG) and behavioural responses to food images for 117 participants. This repository contains raw and processed EEG recordings collected during a food categorisation task and behavioural responses collected during a food go/no-go task and a food paired choice task.

More information, including custom code, food image stimuli, normative ratings, and questionnaire data, can be found via the Open Science Framework (doi.org/10.17605/OSF.IO/Y9PMF/).

FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

Overview

Experiment details

Neural and behavioural data were collected over two testing sessions (N = 117).

In Session 1, participants completed a food categorisation task while EEG was recorded. Participants viewed food images for 2 s and categorised the food (yes/no) as quickly as possible on whether it was healthy, tasty, or whether they were willing to eat it. Each food image was presented three times, once across healthiness, tastiness, and willingness to eat trials. After the food categorisation task, participants provided continuous ratings (0-100) for each food image on healthiness, tastiness, and willingness to eat.

View full README

FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

Overview

Experiment details

Neural and behavioural data were collected over two testing sessions (N = 117).

In Session 1, participants completed a food categorisation task while EEG was recorded. Participants viewed food images for 2 s and categorised the food (yes/no) as quickly as possible on whether it was healthy, tasty, or whether they were willing to eat it. Each food image was presented three times, once across healthiness, tastiness, and willingness to eat trials. After the food categorisation task, participants provided continuous ratings (0-100) for each food image on healthiness, tastiness, and willingness to eat. In Session 2, participants completed two behavioural tasks. The food go/no-go task involved participants making responses to Go food images (e.g., healthy foods) and withholding responses to No-go food images (e.g., not-healthy foods). In the food paired choice task, participants viewed pairs of food images and selected the food that they preferred to eat more out of the pair. Participants’ dietary style, eating motivations, general motivational tendencies and hedonism were assessed using questionnaires.

Separately, normative ratings on the food image stimuli were collected from online samples (total N = 624). We collected continuous ratings (0-100) on 22 food attributes that encompassed nutritive, hedonic, familiarity, taste, and emotional properties of foods.

Data descriptor

For more details about the dataset please see the corresponding paper:

Chae, V. J., Grootswagers, T., Bode, S., & Feuerriegel, D. (2025). FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images (p. 2025.11.07.687287). bioRxiv. https://doi.org/10.1101/2025.11.07.687287

Main files

This dataset was formatted according to the Brain Imaging Data Structure (BIDS). See the dataset_description.json file for the specific version used. ``sub-*/eeg``: contains the raw continuous EEG recording (.bdf) during the food categorisation task and the corresponding metadata file (events.tsv). ``sub-*/beh``: contains the behavioural responses during the food go/no-go task (task-gonogo_beh.tsv) and the food paired choice task (task-pairedchoice_beh.tsv). A copy of each file in .csv format is also contained here. ``derivatives``: contains the cleaned continuous EEG data (.set) for 110 participants used in the technical validation analyses in the data descriptor. Seven participants were excluded from the analyses. The participant IDs and the reason for exclusion are outlined in the metadata file for the participants (participants.tsv).

Additional data

At the end of Session 1, participants rated each food image on healthiness, tastiness, and willingness to eat on a continuous scale (0-100). At the end of Session 2, participants completed questionnaires assessing dietary styles, food motivations, general motivational tendencies, and hedonism.

Separately from online samples (total N = 624), we collected continuous ratings (0-100) for the food images on 22 food attributes, including nutritive properties (healthiness, calorie content, edibility, and level of transformation), hedonic properties (tastiness, willingness to eat, negative and positive valence, and arousal), taste properties (sweetness, saltiness, sourness, bitterness, and savouriness), familiarity (previous exposure, recognisability, and typicality), and elicited emotions (happiness, surprise, disgust, craving, and guilt). Questionnaire responses and continuous rating data can be found via the Open Science Framework (doi.org/10.17605/OSF.IO/Y9PMF/).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=117, range 18–57 yr, mean 26.8 yr)

152025303540455055

Other · 117

Sex composition

Other
117

Channel counts: 65 ch (n=118 recordings)

Sampling frequencies: 512.0 Hz (n=118 recordings)

Total recording duration: 78 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 512 Hz · 117 subjects, 118 recordings

Live trace viewer — sub-021 · task-categorisation

Showing one representative recording out of 117 subjects and 118 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.DS007012(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007012

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

class eegdash.dataset.DS007012(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images

Study:: ds007012 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007012, nan.

Modality: eeg. Subjects: 117; recordings: 118; 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/ds007012 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007012 DOI: https://doi.org/10.18112/openneuro.ds007012.v1.1.0

Examples

>>> from eegdash.dataset import DS007012
>>> dataset = DS007012(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 — DS007012.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 ds007012 to reproduce the tutorial on this dataset.

Citation

Chae, Violet, Grootswagers, Tijl, Bode, Stefan, Feuerriegel, Daniel (2025). FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images. 10.18112/openneuro.ds007012.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007012.v1.1.0.

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DS003474EEG · 122 subj DS003518EEG · 110 subj DS004532EEG · 110 subj DS003478EEG · 122 subj DS004579EEG · 139 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events

Provenance

CC0 · 10.18112/openneuro.ds007012.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007012`
Title	FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images
Author (year)	—
Canonical	—
Importable as	`DS007012`
Year	2025
Authors	Chae, Violet, Grootswagers, Tijl, Bode, Stefan, Feuerriegel, Daniel
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007012.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007012: eeg dataset, 117 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#