EEGdash›OpenNeuro›DS006367
Iss. 6367 · 52 subjects · 52 recordings · CC0
Dataset Brief · Memory Reactivation Levels Remain Unaffected by Anticipated I…

DS006367: eeg dataset, 52 subjects#

Memory Reactivation Levels Remain Unaffected by Anticipated Interference

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

52-participant EEG dataset — Memory Reactivation Levels Remain Unaffected by Anticipated Interference.

Data & curation xx
EEG · 30 ch1000 HzBIDS v1.2.1Task · DelayedMatchtoSampleTaskHealthyVisualMemory
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#

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},
}
§ 02Study · The README

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)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 30 ch (n=52 recordings)

Sampling frequencies: 1000.0 Hz (n=52 recordings)

Total recording duration: 68 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 30 ch · EEG · 1000 Hz · 52 subjects, 52 recordings
Live trace viewer — sub-302 · task-DelayedMatchtoSampleTask

Showing one representative recording out of 52 subjects and 52 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 · 30 sensors — 30 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.

NEMAR pipeline statistics — DS006367
HED event descriptors word cloud HED event descriptors word cloud — DS006367
§ 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…
Full dataset metadata table

Dataset ID

DS006367

Title

Memory Reactivation Levels Remain Unaffected by Anticipated Interference

Author (year)

DS6367_Memory_Reactivation

Canonical

—

Importable as

DS006367, DS6367_Memory_Reactivation

Year

—

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},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006367(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)DS6367_Memory_Reactivation
Canonical—
Importable asDS006367 · DS6367_Memory_Reactivation
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006367(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Memory Reactivation Levels Remain Unaffected by Anticipated Interference

Study:

ds006367 (OpenNeuro)

Author (year):

DS6367_Memory_Reactivation

Canonical:

—

Also importable as: DS006367, DS6367_Memory_Reactivation.

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 DOI: https://doi.org/10.18112/openneuro.ds006367.v1.0.1

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: 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/ds006367 · pull with datasets.load_dataset("EEGDash/ds006367").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006367.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 ds006367 to reproduce the tutorial on this dataset.

Citation

xx (n.d.). Memory Reactivation Levels Remain Unaffected by Anticipated Interference. 10.18112/openneuro.ds006367.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006367.v1.0.1.

BIDS
BIDS v1.2.1
Sidecars
events · channels · electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#