DS006367#

Memory Reactivation Levels Remain Unaffected by Anticipated Interference

Access recordings and metadata through EEGDash.

Citation: xx (2025). Memory Reactivation Levels Remain Unaffected by Anticipated Interference. 10.18112/openneuro.ds006367.v1.0.1

Modality: eeg Subjects: 52 Recordings: 369 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006367

dataset = DS006367(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006367(cache_dir="./data", subject="01")

Advanced query

dataset = DS006367(
    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{ds006367,
  title = {Memory Reactivation Levels Remain Unaffected by Anticipated Interference},
  author = {xx},
  doi = {10.18112/openneuro.ds006367.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006367.v1.0.1},
}

About This Dataset#

In this memory interference task, each trial began with a space key press, followed by a fixation dot for 1000–1500 ms. Then, two lateral objects appeared: a cued target (learned) and an irrelevant novel one. Participants memorized the cued object. After a 1400 ms delay, interference objects appeared briefly on half the blocks; otherwise, only fixation. A probe then showed two objects vertically aligned, and participants selected the target using arrow keys. Feedback followed the response, showing the correct object with color-coded text for 1000 ms. The preprocessing steps to reach this dataset is explained in the following preprint and the mentioned OSF repository xx (Experiment 1)

Dataset Information#

Dataset ID

DS006367

Title

Memory Reactivation Levels Remain Unaffected by Anticipated Interference

Year

2025

Authors

xx

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006367.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006367,
  title = {Memory Reactivation Levels Remain Unaffected by Anticipated Interference},
  author = {xx},
  doi = {10.18112/openneuro.ds006367.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006367.v1.0.1},
}

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: 52

  • Recordings: 369

  • Tasks: 1

Channels & sampling rate

  • Channels: 28 (52), 30 (52)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Memory

Files & format

  • Size on disk: 27.8 GB

  • File count: 369

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006367.v1.0.1

Provenance

API Reference#

Use the DS006367 class to access this dataset programmatically.

class eegdash.dataset.DS006367(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006367. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 52; recordings: 52; 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/ds006367 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006367

Examples

>>> from eegdash.dataset import DS006367
>>> dataset = DS006367(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#