EEGdash›OpenNeuro›DS008092
Iss. 8092 · 77 subjects · 77 recordings · CC0
Dataset Brief · Food-image categorization with consumer EEG (vegetarian vs. m…

DS008092: eeg dataset, 77 subjects#

Food-image categorization with consumer EEG (vegetarian vs. meat)

Access recordings and metadata through EEGDash.

Citation: [ADD AUTHORS] (2026). Food-image categorization with consumer EEG (vegetarian vs. meat). 10.18112/openneuro.ds008092.v1.0.2

Modality: eeg Subjects: 77 Recordings: 77 License: CC0 Source: openneuro

Metadata: Complete (100%)

77-participant EEG dataset — Food-image categorization with consumer EEG (vegetarian vs. meat).

Data & curation [ADD AUTHORS]
Senior author [ADD AUTHORS]
Year 2026 · Distributed via OpenNeuro
EEG · 14 ch128 HzBIDS 1.9.0Task · foodcat
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS008092

dataset = DS008092(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS008092(cache_dir="./data", subject="01")

Advanced query

dataset = DS008092(
    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{ds008092,
  title = {Food-image categorization with consumer EEG (vegetarian vs. meat)},
  author = {[ADD AUTHORS]},
  doi = {10.18112/openneuro.ds008092.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds008092.v1.0.2},
}
§ 02Study · The README

About This Dataset#

14-channel EEG (EMOTIV EPOC X / EPOC+, scalp positions AF3 F7 F3 FC5 T7 P7 O1

O2 P8 T8 FC6 F4 F8 AF4) recorded while participants categorized prepared-food images as meat or vegetarian. Each recording also includes a short eyes-open / eyes-closed resting baseline that precedes the task.

These data were collected within longer sessions that also recorded other

measures (reported elsewhere). Only the categorization paradigm and its resting baseline are released here.

Food-image categorization with consumer EEG (vegetarian vs. meat)

Overview

Task

The same set of 37 prepared-food images was presented twice (up to ~74 trials).

On each trial a participant pressed M (meat) or X (vegetarian). Response times

View full README

Food-image categorization with consumer EEG (vegetarian vs. meat)

Overview

Task

The same set of 37 prepared-food images was presented twice (up to ~74 trials).

On each trial a participant pressed M (meat) or X (vegetarian). Response times were recorded.

In the events files: - stim_category is the displayed image’s true category: m (meat) or v (vegetarian). - response_key is the physical key pressed: M or X. - response is that key mapped back to category (m/v) so it lines up with stim_category. - accuracy is 1 when response matches stim_category.

Files

  • participants.tsv / participants.json: one row per participant, with demographics, baseline questionnaire, derived dietary indicators, and per-subject task summaries. .tsv is tab-separated and opens like a CSV.

  • sub-NN/eeg/: the trimmed recording (_eeg.edf), _events.tsv, _channels.tsv (with per-channel contact-quality), and _eeg.json sidecar.

Data quality

Data are released minimally processed: no filtering, no artifact rejection, no ICA. Per-channel EMOTIV contact-quality values are provided in each channels.tsv so re-users can apply their own quality criteria. The resting baseline supports a per-participant eyes-closed alpha-reactivity check.

Notes for re-use

  • EEG is unfiltered (sidecars report SoftwareFilters: n/a).

  • All recordings are sampled at 128 Hz.

  • Event labels are embedded in each EDF as EDF+ annotations (rest_eyesopen, rest_eyesclosed, categorize/meat, categorize/veg), so any reader displays them directly. The events.tsv holds the full trial-level record (response, response time, accuracy).

  • Resting segments were advanced by participant button-press; a fixed 30 s window inside each instructed eyes-open and eyes-closed segment is released.

  • Recording timestamps have been removed; recordings carry a neutral fixed date.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=77, range 19–49 yr, mean 30.1 yr)

15202530354045
Other · 77

Sex composition

77
subjects
Female
26
Male
51
F : M ratio
0.51 : 1
34% female · n = 77 subjects with reported sex.
HandednessAmbidextrous · 2

Channel counts: 14 ch (n=77 recordings)

Sampling frequencies: 128.0 Hz (n=77 recordings)

Total recording duration: 5 h 34 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 14 ch · EEG · 128 Hz · 77 subjects, 77 recordings
Live trace viewer — sub-17 · task-foodcat

Showing one representative recording out of 77 subjects and 77 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.

Electrode layout — EEG · 14 sensors — 14 channels

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.

NEMAR pipeline statistics — DS008092
HED event descriptors word cloud HED event descriptors word cloud — DS008092
§ 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…
Full dataset metadata table

Dataset ID

DS008092

Title

Food-image categorization with consumer EEG (vegetarian vs. meat)

Author (year)

—

Canonical

—

Importable as

DS008092

Year

2026

Authors

[ADD AUTHORS]

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds008092.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds008092,
  title = {Food-image categorization with consumer EEG (vegetarian vs. meat)},
  author = {[ADD AUTHORS]},
  doi = {10.18112/openneuro.ds008092.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds008092.v1.0.2},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS008092(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asDS008092
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS008092(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Food-image categorization with consumer EEG (vegetarian vs. meat)

Study:

ds008092 (OpenNeuro)

Author (year):

—

Canonical:

—

Also importable as: DS008092.

Modality: eeg; Subject type: Unknown. Subjects: 77; recordings: 77; 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/ds008092 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008092 DOI: https://doi.org/10.18112/openneuro.ds008092.v1.0.2

Examples

>>> from eegdash.dataset import DS008092
>>> dataset = DS008092(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 — DS008092.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 ds008092 to reproduce the tutorial on this dataset.

Citation

[ADD AUTHORS] (2026). Food-image categorization with consumer EEG (vegetarian vs. meat). 10.18112/openneuro.ds008092.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds008092.v1.0.2.

BIDS
BIDS 1.9.0
Sidecars
events · channels · electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#