DS007431: eeg dataset, 47 subjects#

Diffuse predictions stabilize and reshape neural code during memory encoding

Access recordings and metadata through EEGDash.

Citation: Nursena Ataseven, Sahcan Ozdemir, Wouter Kruijne, Daniel Schneider, Elkan G. Akyurek (2026). Diffuse predictions stabilize and reshape neural code during memory encoding. 10.18112/openneuro.ds007431.v1.0.0

Modality: eeg Subjects: 47 Recordings: 47 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007431

dataset = DS007431(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007431(cache_dir="./data", subject="01")

Advanced query

dataset = DS007431(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds007431,
  title = {Diffuse predictions stabilize and reshape neural code during memory encoding},
  author = {Nursena Ataseven and Sahcan Ozdemir and Wouter Kruijne and Daniel Schneider and Elkan G. Akyurek},
  doi = {10.18112/openneuro.ds007431.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007431.v1.0.0},
}

About This Dataset#

Experimental task: participants judged whether a probe grating was rotated clockwise or counterclockwise relative to a memorized orientation, which was either predictable or unpredictable. Each memory item was preceded by a central color cue (red, green, or blue). In half of the trials, two of these colors (predictive) cued two non-overlapping 90° segments of orientations that the grating was sampled from. Thus, participants knew the range of possible orientations of these items, but not their exact orientation. In the other half of the trials, a third (non-predictive) color was presented that signaled the item could have any possible orientation.The preprocessing and analysis scripts can be found on OSF: https://osf.io/8evwh/

Dataset Information#

Dataset ID

DS007431

Title

Diffuse predictions stabilize and reshape neural code during memory encoding

Author (year)

Ataseven2026

Canonical

Ataseven2024

Importable as

DS007431, Ataseven2026, Ataseven2024

Year

2026

Authors

Nursena Ataseven, Sahcan Ozdemir, Wouter Kruijne, Daniel Schneider, Elkan G. Akyurek

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007431.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007431,
  title = {Diffuse predictions stabilize and reshape neural code during memory encoding},
  author = {Nursena Ataseven and Sahcan Ozdemir and Wouter Kruijne and Daniel Schneider and Elkan G. Akyurek},
  doi = {10.18112/openneuro.ds007431.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007431.v1.0.0},
}

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: 47

  • Recordings: 47

  • Tasks: 1

Channels & sampling rate

  • Channels: 66

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 160.1695947222222

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Memory

Files & format

  • Size on disk: 144.6 GB

  • File count: 47

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007431.v1.0.0

Provenance

API Reference#

Use the DS007431 class to access this dataset programmatically.

class eegdash.dataset.DS007431(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Diffuse predictions stabilize and reshape neural code during memory encoding

Study:

ds007431 (OpenNeuro)

Author (year):

Ataseven2026

Canonical:

Ataseven2024

Also importable as: DS007431, Ataseven2026, Ataseven2024.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 47; recordings: 47; 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/ds007431 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007431 DOI: https://doi.org/10.18112/openneuro.ds007431.v1.0.0

Examples

>>> from eegdash.dataset import DS007431
>>> dataset = DS007431(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

See Also#