DS006576#

The role of REM sleep in neural differentiation of memories in the hippocampus

Access recordings and metadata through EEGDash.

Citation: Elizabeth A. McDevitt, Ghootae Kim, Nicholas B. Turk-Browne, Kenneth A. Norman (2025). The role of REM sleep in neural differentiation of memories in the hippocampus. 10.18112/openneuro.ds006576.v1.0.2

Modality: eeg Subjects: 49 Recordings: 4200 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds006576.v1.0.2},
}

About This Dataset#

NOTE: This version contains datasets for 49 of the 69 participants. New versions will be created as more data are uploaded.

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.

Dataset Information#

Dataset ID	`DS006576`
Title	The role of REM sleep in neural differentiation of memories in the hippocampus
Year	2025
Authors	Elizabeth A. McDevitt, Ghootae Kim, Nicholas B. Turk-Browne, Kenneth A. Norman
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006576.v1.0.2
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: 49
Recordings: 4200
Tasks: 8

Channels & sampling rate

Channels: 73 (48), 64 (27)
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS006576 class to access this dataset programmatically.

class eegdash.dataset.DS006576(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006576. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 48; recordings: 48; 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/ds006576 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006576

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, 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