EEGdash›OpenNeuro›DS004252

Iss. 4252 · 16 subjects · 16 recordings · CC0

Dataset Brief · Rotation-tolerant representations elucidate the time course o…

DS004252: eeg dataset, 16 subjects#

Rotation-tolerant representations elucidate the time course of high-level object processing

Citation: Denise Moerel, Tijl Grootswagers, Amanda K. Robinson, Patrick Engeler, Alex O. Holcombe, Thomas A. Carlson (20). Rotation-tolerant representations elucidate the time course of high-level object processing. 10.18112/openneuro.ds004252.v1.1.0

16-participant EEG dataset — Rotation-tolerant representations elucidate the time course of high-level object processing.

Data & curation Denise Moerel · Tijl Grootswagers · Amanda K. Robinson · Patrick Engeler · Alex O. Holcombe · Thomas A. Carlson
Year 20 · Distributed via OpenNeuro

EEG · 128 ch1000 HzBIDS 1.6.0Task · rsvpHealthyVisualPerception

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 DS004252

dataset = DS004252(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 = DS004252(cache_dir="./data", subject="01")

Advanced query

dataset = DS004252(
    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{ds004252,
  title = {Rotation-tolerant representations elucidate the time course of high-level object processing},
  author = {Denise Moerel and Tijl Grootswagers and Amanda K. Robinson and Patrick Engeler and Alex O. Holcombe and Thomas A. Carlson},
  doi = {10.18112/openneuro.ds004252.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004252.v1.1.0},
}

§ 02Study · The README

About This Dataset#

This dataset contains electroencephalography (EEG) recordings from 16 participants viewing object images presented in eight different 2-D rotations. Stimuli were shown in rapid visual streams at two presentation rates (5 Hz and 20 Hz). EEG data are in standard bids format.

The pre-print can be found here: https://doi.org/10.31234/osf.io/wp73u

The analysis codes, results, and figures can be found on OSF: Moerel, D., Grootswagers, T., Robinson, A. K., Engeler, P., Holcombe, A. O., & Carlson, T. A. (2026, April 14). Rotation-tolerant representations elucidate the time course of high-level object processing. https://doi.org/10.17605/OSF.IO/R93ES

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=16, range 19–60 yr, mean 24.8 yr)

1520253060

Other · 16

Sex composition

Female
14
Male
2
F : M ratio
7.00 : 1

88% female · n = 16 subjects with reported sex.

HandednessRight · 14Left · 2

Channel counts: 128 ch (n=16 recordings)

Sampling frequencies: 1000.0 Hz (n=16 recordings)

Total recording duration: 10 h 42 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 1000 Hz · 16 subjects, 16 recordings

Live trace viewer — sub-01 · task-rsvp

Showing one representative recording out of 16 subjects and 16 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.DS004252(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Moerel2022_Rotation

Canonical—

Importable asDS004252 · Moerel2022_Rotation

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

class eegdash.dataset.DS004252(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Rotation-tolerant representations elucidate the time course of high-level object processing

Study:: ds004252 (OpenNeuro)
Author (year):: Moerel2022_Rotation
Canonical:: —

Also importable as: DS004252, Moerel2022_Rotation.

Modality: eeg. Subjects: 16; recordings: 16; 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/ds004252 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004252 DOI: https://doi.org/10.18112/openneuro.ds004252.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004252
>>> dataset = DS004252(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 FacePre-bundled mirror at EEGDash/ds004252 · pull with datasets.load_dataset("EEGDash/ds004252").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004252.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 ds004252 to reproduce the tutorial on this dataset.

Citation

Denise Moerel, Tijl Grootswagers, Amanda K. Robinson, Patrick Engeler, Alex O. Holcombe, … (20). Rotation-tolerant representations elucidate the time course of high-level object processing. 10.18112/openneuro.ds004252.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.ds004252.v1.1.0.

Related & sibling datasets

DS004284EEG · 18 subj DS004554EEG · 16 subj DS004306EEG · 12 subj DS004357EEG · 16 subj DS003887EEG · 24 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.6.0

Sidecars

events

Provenance

CC0 · 10.18112/openneuro.ds004252.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004252`
Title	Rotation-tolerant representations elucidate the time course of high-level object processing
Author (year)	`Moerel2022_Rotation`
Canonical	—
Importable as	`DS004252`, `Moerel2022_Rotation`
Year	20
Authors	Denise Moerel, Tijl Grootswagers, Amanda K. Robinson, Patrick Engeler, Alex O. Holcombe, Thomas A. Carlson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004252.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS004252: eeg dataset, 16 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#