EEGdash›NeMAR›ON007964

Iss. 7964 · 19 subjects · 19 recordings · CC0

Dataset Brief · Disentangling objects' contextual associations from perceptua…

ON007964: eeg dataset, 19 subjects#

Disentangling objects’ contextual associations from perceptual and conceptual attributes using time-resolved neural decoding

Access recordings and metadata through EEGDash.

Citation: Kim, Ariel*, Quek, Genevieve*, Moerel, Denise*, Gorton, Olivia, Carlson, Thomas (20). Disentangling objects’ contextual associations from perceptual and conceptual attributes using time-resolved neural decoding. 10.82901/nemar.on007964

Modality: eeg Subjects: 19 Recordings: 19 License: CC0 Source: nemar

Metadata: Complete (100%)

19-participant EEG dataset — Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding.

Data & curation Kim, Ariel · Quek, Genevieve · Moerel, Denise · Gorton, Olivia · Carlson, Thomas
Senior author Carlson, Thomas
Year 20 · Distributed via NeMAR
Funding ARC DP200101787 (TAC)

EEG · 128 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 ON007964

dataset = ON007964(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 = ON007964(cache_dir="./data", subject="01")

Advanced query

dataset = ON007964(
    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{on007964,
  title = {Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding},
  author = {Kim, Ariel* and Quek, Genevieve* and Moerel, Denise* and Gorton, Olivia and Carlson, Thomas},
  doi = {10.82901/nemar.on007964},
  url = {https://doi.org/10.82901/nemar.on007964},
}

§ 02Study · The README

About This Dataset#

Raw EEG data and representational dissimilarity matrices can be found here.

Analysis scripts and behavioural data can be found via the open science framework: https://doi.org/10.17605/OSF.IO/JY284

A preprint of the manuscript can be found on biorxiv: https://doi.org/10.1101/2025.05.29.656895 Experiment Details: We recorded 128 channel EEG data from 20 observers as they viewed object images appearing at a rate of 3.33 Hz (image duration = 100 ms; ISI = 200 ms). One participant (sub-02) was excluded due to a technical error in the EEG recording, resulting in a final sample of 19 participants.

DOI

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=19, range 18–34 yr, mean 21.9 yr)

15202530

Other · 19

Sex composition

Female
15
Male
4
Other
1
F : M ratio
3.75 : 1

75% female · n = 20 subjects with reported sex.

HandednessRight · 19Left · 1

Channel counts: 128 ch (n=19 recordings)

Sampling frequencies: 1000.0 Hz (n=19 recordings)

Total recording duration: 10 h 56 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 1000 Hz · 19 subjects, 19 recordings

Live trace viewer — sub-17 · task-rsvp

Showing one representative recording out of 19 subjects and 19 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.ON007964(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON007964

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

class eegdash.dataset.ON007964(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Disentangling objects’ contextual associations from perceptual and conceptual attributes using time-resolved neural decoding

Study:: on007964 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: ON007964.

Modality: eeg; Subject type: Unknown. Subjects: 19; recordings: 19; 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/on007964 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007964 DOI: https://doi.org/10.82901/nemar.on007964

Examples

>>> from eegdash.dataset import ON007964
>>> dataset = ON007964(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 — ON007964.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 on007964 to reproduce the tutorial on this dataset.

Citation

Kim, Ariel, Quek, Genevieve, Moerel, Denise, Gorton, Olivia, Carlson, Thomas (20). Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding. 10.82901/nemar.on007964

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on007964.

Related & sibling datasets

ON006695EEG · 19 subj ON004995EEG · 20 subj ON006593EEG · 21 subj ON004252EEG · 16 subj ON003885EEG · 24 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events

Provenance

CC0 · 10.82901/nemar.on007964

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON007964`
Title	Disentangling objects’ contextual associations from perceptual and conceptual attributes using time-resolved neural decoding
Author (year)	—
Canonical	—
Importable as	`ON007964`
Year	20
Authors	Kim, Ariel, Quek, Genevieve, Moerel, Denise*, Gorton, Olivia, Carlson, Thomas
License	CC0
Citation / DOI	10.82901/nemar.on007964
Source links	OpenNeuro \| NeMAR \| Source URL

ON007964: eeg dataset, 19 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#