DS006576: eeg dataset, 67 subjects#
The role of REM sleep in neural differentiation of memories in the hippocampus
Citation: Elizabeth A. McDevitt, Ghootae Kim, Nicholas B. Turk-Browne, Kenneth A. Norman (2026). The role of REM sleep in neural differentiation of memories in the hippocampus. 10.18112/openneuro.ds006576.v1.0.5
67-participant EEG dataset — The role of REM sleep in neural differentiation of memories in the hippocampus.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006576
dataset = DS006576(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006576(cache_dir="./data", subject="01")
Advanced query
dataset = DS006576(
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{ds006576,
title = {The role of REM sleep in neural differentiation of memories in the hippocampus},
author = {Elizabeth A. McDevitt and Ghootae Kim and Nicholas B. Turk-Browne and Kenneth A. Norman},
doi = {10.18112/openneuro.ds006576.v1.0.5},
url = {https://doi.org/10.18112/openneuro.ds006576.v1.0.5},
}
About This Dataset#
This dataset contains the fMRI and EEG data for E.A. McDevitt, G. Kim, N.B. Turk-Browne, K.A. Norman (2026). The role of rapid eye movement sleep in neural differentiation of memories in the hippocampus. Journal of Cognitive Neuroscience, 10.1162/jocn.a.82
Please refer to the paper for detailed methods.
The dataset includes 69 participants with three fMRI scans and one EEG session per participant. Depending on the participant’s condition, the EEG session either contains sleep data from a nap or data recorded during a quiet wake session.
Please contact Elizabeth McDevitt (emcdevitt@princeton.edu) if you have any questions.
Notes about the dataset:
The following subjects/sessions do not include a T1w anatomical scan: sub-160 ses-00; sub-170 ses-00; sub-178 ses-01 - sub-107/ses-02/func: There are three runs of the decision task included instead of two. During decision_run-01, the participant did not respond to ‘B’ trials (coded in column trial_type). Therefore, there are many trials with no response_accuracy or response_times recorded in task-decision_run-01_events.tsv. Immediately following this run, the same task was re-run as decision_run-03 to collect behavioral responses; therefore the data associated with task-decision_run-03 can be considered a “repeat” of task-decision_run-01. Decision_run-02 was run as expected during the second cycle of the reward prediction task. - sub-108_ses-02_task-reward_run-01_events.tsv: Many trials have no response_accuracy or response_time recorded. The participant misunderstood instructions and did not respond on trials where they predicted a “neutral” outcome. - sub-182_ses-01_task-study_run-01_events.tsv: There was an issue with Matlab not recording “2” button presses during this run of the task. The experimenter recoded all “no response” trials as “2” and used this to code response_accuracy. However, there were no response_times recorded for these trials.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 73 ch (n=67 recordings)
Sampling frequencies: 512.0 Hz (n=67 recordings)
Total recording duration: 116 h
Signal · Electrodes & live trace#
Live trace viewer — sub-137 · task-rest
Showing one representative recording out of
67 subjects and 67 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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
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.
Full dataset metadata table
Dataset ID |
|
Title |
The role of REM sleep in neural differentiation of memories in the hippocampus |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2026 |
Authors |
Elizabeth A. McDevitt, Ghootae Kim, Nicholas B. Turk-Browne, Kenneth A. Norman |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006576,
title = {The role of REM sleep in neural differentiation of memories in the hippocampus},
author = {Elizabeth A. McDevitt and Ghootae Kim and Nicholas B. Turk-Browne and Kenneth A. Norman},
doi = {10.18112/openneuro.ds006576.v1.0.5},
url = {https://doi.org/10.18112/openneuro.ds006576.v1.0.5},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006576 · McDevitt2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006576(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
The role of REM sleep in neural differentiation of memories in the hippocampus
- Study:
ds006576(OpenNeuro)- Author (year):
McDevitt2025- Canonical:
—
Also importable as:
DS006576,McDevitt2025.Modality:
eeg; Experiment type:Sleep; Subject type:Healthy. Subjects: 67; recordings: 67; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds006576 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006576 DOI: https://doi.org/10.18112/openneuro.ds006576.v1.0.5
Examples
>>> from eegdash.dataset import DS006576 >>> dataset = DS006576(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds006576").huggingface
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 ds006576 to reproduce the tutorial on this dataset.
Citation
Elizabeth A. McDevitt, Ghootae Kim, Nicholas B. Turk-Browne, Kenneth A. Norman (2026). The role of REM sleep in neural differentiation of memories in the hippocampus. 10.18112/openneuro.ds006576.v1.0.5
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006576.v1.0.5.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset