EEGdash›OpenNeuro›DS007081

Iss. 7081 · 41 subjects · 41 recordings · CC0

Dataset Brief · Passive but accessible

DS007081: eeg dataset, 41 subjects#

Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load

Citation: Yakup Yılmaz, Nursena Ataseven Özdemir, Wouter Kruijne, Elkan Akyürek, Eren Günseli (—). Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load. 10.18112/openneuro.ds007081.v1.0.0

41-participant EEG dataset — Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load.

Data & curation Yakup Yılmaz · Nursena Ataseven Özdemir · Wouter Kruijne · Elkan Akyürek · Eren Günseli

EEG · 32 ch1000 HzBIDS 1.10.1Task · PassiveAccessibleHealthyVisualMemory

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 DS007081

dataset = DS007081(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 = DS007081(cache_dir="./data", subject="01")

Advanced query

dataset = DS007081(
    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{ds007081,
  title = {Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load},
  author = {Yakup Yılmaz and Nursena Ataseven Özdemir and Wouter Kruijne and Elkan Akyürek and Eren Günseli},
  doi = {10.18112/openneuro.ds007081.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007081.v1.0.0},
}

§ 02Study · The README

About This Dataset#

Each trial began with a fixation dot presented for a jittered intertrial interval (ITI) between 600 and 1000 ms.

The first memory screen (1000 ms) showed two objects on one lateral side that participants were instructed to memorize (indicated by a wedge cue), and two objects on the opposite side to balance visual input.

Depending on the block condition, the to-be-memorized objects on the first screen could be studied (learned in the learning phase) or novel/unstudied.

After a 1400 ms interstimulus interval, a second memory screen (1000 ms) presented additional items vertically around fixation (one above and one below fixation); these items were always novel/unstudied and were placed near fixation to avoid influencing lateral EEG indices from the first screen. In the extra-load expectation condition, additional second-screen items appeared on 80% of trials (and were omitted on 20% of trials), whereas in the low-load expectation condition this probability was reversed (20% appear, 80% omitted).

After a 400 ms interstimulus interval, a probe from either the first or second memory screen was presented and participants reported the probed object’s color by moving the mouse; the probe color updated continuously along an invisible color wheel whose orientation was randomly rotated on each trial. After the response, absolute angular error feedback was displayed for 400 ms; for studied objects, if the error exceeded 40°, the correct color was displayed for 1000 ms as corrective feedback.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=41 recordings)

Sampling frequencies: 1000.0 Hz (n=41 recordings)

Total recording duration: 26 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 1000 Hz · 41 subjects, 41 recordings

Live trace viewer — sub-021 · task-PassiveAccessible

Showing one representative recording out of 41 subjects and 41 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 · 32 sensors — 32 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.

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.DS007081(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Ylmaz2025

Canonical—

Importable asDS007081 · Ylmaz2025

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

class eegdash.dataset.DS007081(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load

Study:: ds007081 (OpenNeuro)
Author (year):: Ylmaz2025
Canonical:: —

Also importable as: DS007081, Ylmaz2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 41; recordings: 41; 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/ds007081 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007081 DOI: https://doi.org/10.18112/openneuro.ds007081.v1.0.0

Examples

>>> from eegdash.dataset import DS007081
>>> dataset = DS007081(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 — DS007081.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 ds007081 to reproduce the tutorial on this dataset.

Citation

Yakup Yılmaz, Nursena Ataseven Özdemir, Wouter Kruijne, Elkan Akyürek, Eren Günseli (n.d.). Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load. 10.18112/openneuro.ds007081.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007081.v1.0.0.

Related & sibling datasets

DS006367EEG · 52 subj DS006963EEG · 32 subj DS006848EEG · 30 subj DS005697EEG · 51 subj DS007431EEG · 47 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.1

Sidecars

channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007081.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007081`
Title	Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load
Author (year)	`Ylmaz2025`
Canonical	—
Importable as	`DS007081`, `Ylmaz2025`
Year	—
Authors	Yakup Yılmaz, Nursena Ataseven Özdemir, Wouter Kruijne, Elkan Akyürek, Eren Günseli
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007081.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007081: eeg dataset, 41 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#