EEGdash›NeMAR›ON003825

Iss. 3825 · 50 subjects · 50 recordings · CC0

Dataset Brief · Human electroencephalography recordings from 50 subjects for…

ON003825: eeg dataset, 50 subjects#

Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts

Citation: Grootswagers, Tijl, Zhou, Ivy, Robinson, Amanda, Hebart, Martin, Carlson, Thomas (20). Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts. 10.82901/nemar.on003825

50-participant EEG dataset — Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts.

Data & curation Grootswagers, Tijl · Zhou, Ivy · Robinson, Amanda · Hebart, Martin · Carlson, Thomas
Year 20 · Distributed via NeMAR
Funding ARC DP160101300 (TAC) · ARC DP200101787 (TAC) · + 1 more

EEG · 63 (48), 128 (2) ch1000 HzBIDS 1.0.2Task · rsvp

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 ON003825

dataset = ON003825(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 = ON003825(cache_dir="./data", subject="01")

Advanced query

dataset = ON003825(
    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{on003825,
  title = {Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts},
  author = {Grootswagers, Tijl and Zhou, Ivy and Robinson, Amanda and Hebart, Martin and Carlson, Thomas},
  doi = {10.82901/nemar.on003825},
  url = {https://doi.org/10.82901/nemar.on003825},
}

§ 02Study · The README

About This Dataset#

Experiment Details

Human electroencephalography recordings from 50 subjects for 1,854 concepts and 22,248 images in the THINGS stimulus database.

Images were presented in rapid serial visual presentation streams at 10Hz rates. Participants performed an orthogonal fixation colour change detection task.

Experiment length: 1 hour

DOI

Stimuli

The 22,248 stimulus images are NOT bundled with this BIDS dataset: the THINGS image archive (Hebart et al. 2019) is licensed for research / non-commercial use and may not be redistributed without consent of the copyright owners. Acquire the archive directly from the canonical OSF project (https://osf.io/jum2f/, file images_THINGS.zip, ~5 GB, password documented at osf.io/j6a3m), and unpack it under
View full README
DOI

Stimuli

The 22,248 stimulus images are NOT bundled with this BIDS dataset: the THINGS image archive (Hebart et al. 2019) is licensed for research / non-commercial use and may not be redistributed without consent of the copyright owners. Acquire the archive directly from the canonical OSF project (https://osf.io/jum2f/, file images_THINGS.zip, ~5 GB, password documented at osf.io/j6a3m), and unpack it under stimuli/<concept>/<file>.jpg.

Once the images are in place, every events.tsv stim_file column entry resolves to a real file. Two helpers are provided:
1) one-off normalisation: rewrites events.tsv to BIDS-canonical form

(adds stim_file, drops stim/stimname, converts onset/duration to seconds)

python code/normalize_events_to_bids.py

2) sanity check after stimuli are placed

python code/smoke_test.py
import pandas as pd
from code.align_stimuli import StimulusAligner
aligner = StimulusAligner('.')
events = pd.read_csv('sub-01/eeg/sub-01_task-rsvp_events.tsv', sep='\t')
paths = aligner.paths_for_events(events)   # list[Path | None]
See stimuli/README for the license terms and stimuli/stim-things_image.json for the BIDS stimulus sidecar.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=50, range 17–30 yr, mean 20.4 yr)

15202530

Other · 50

Sex composition

Female
36
Male
14
F : M ratio
2.57 : 1

72% female · n = 50 subjects with reported sex.

Channel counts (ch)

63128

Sampling frequencies: 1000.0 Hz (n=50 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 (48), 128 (2) ch · EEG · 1000 Hz · 50 subjects, 50 recordings

Live trace viewer — sub-01 · task-rsvp

Showing one representative recording out of 50 subjects and 50 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.ON003825(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON003825

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

class eegdash.dataset.ON003825(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts

Study:: on003825 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON003825, nan.

Modality: eeg. Subjects: 50; recordings: 50; 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/on003825 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on003825 DOI: https://doi.org/10.82901/nemar.on003825

Examples

>>> from eegdash.dataset import ON003825
>>> dataset = ON003825(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 — ON003825.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 on003825 to reproduce the tutorial on this dataset.

Citation

Grootswagers, Tijl, Zhou, Ivy, Robinson, Amanda, Hebart, Martin, Carlson, Thomas (20). Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts. 10.82901/nemar.on003825

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on003825.

Related & sibling datasets

ON007181EEG · 59 subj ON007176EEG · 45 subj NM000180EEG · 45 subj NM000132EEG · 40 subj ON004018EEG · 16 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events · eeg.json

Provenance

CC0 · 10.82901/nemar.on003825

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON003825`
Title	Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts
Author (year)	—
Canonical	—
Importable as	`ON003825`
Year	20
Authors	Grootswagers, Tijl, Zhou, Ivy, Robinson, Amanda, Hebart, Martin, Carlson, Thomas
License	CC0
Citation / DOI	10.82901/nemar.on003825
Source links	OpenNeuro \| NeMAR \| Source URL

ON003825: eeg dataset, 50 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#