DS007081#

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

Access recordings and metadata through EEGDash.

Citation: Yakup Yılmaz, Nursena Ataseven Özdemir, Wouter Kruijne, Elkan Akyürek, Eren Günseli (2025). 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

Modality: eeg Subjects: 41 Recordings: 168 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

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.

Dataset Information#

Dataset ID	`DS007081`
Title	Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load
Year	2025
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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 41
Recordings: 168
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS007081 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds007081. 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None