EEGdash›OpenNeuro›DS005530

Iss. 5530 · 17 subjects · 21 recordings · CC0

Dataset Brief · Depotentiation of emotional reactivity using TMR during REM s…

DS005530: eeg dataset, 17 subjects#

Depotentiation of emotional reactivity using TMR during REM sleep

Citation: Viviana Greco, Tamas A. Foldes, Neil A. Harrison, Kevin Murphy, Marta Wawrzuta, Mahmoud E. A. Abdellahi, Penelope A. Lewis (—). Depotentiation of emotional reactivity using TMR during REM sleep. 10.18112/openneuro.ds005530.v1.0.9

17-participant EEG dataset — Depotentiation of emotional reactivity using TMR during REM sleep.

Data & curation Viviana Greco · Tamas A. Foldes · Neil A. Harrison · Kevin Murphy · Marta Wawrzuta · Mahmoud E. A. Abdellahi · …
Funding ERC consolidator grant SolutionSleep to Lewis P.A. · The Wellcome Trust [WT224267] to Murphy K

EEG · 10 ch500 HzBIDS 1.8.0Task · sleepHealthyMultisensorySleep

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005530

dataset = DS005530(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 = DS005530(cache_dir="./data", subject="01")

Advanced query

dataset = DS005530(
    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{ds005530,
  title = {Depotentiation of emotional reactivity using TMR during REM sleep},
  author = {Viviana Greco and Tamas A. Foldes and Neil A. Harrison and Kevin Murphy and Marta Wawrzuta and Mahmoud E. A. Abdellahi and Penelope A. Lewis},
  doi = {10.18112/openneuro.ds005530.v1.0.9},
  url = {https://doi.org/10.18112/openneuro.ds005530.v1.0.9},
}

§ 02Study · The README

About This Dataset#

This dataset contains fMRI and EEG data from a study investigating the effects of Targeted Memory Reactivation (TMR) during REM sleep on emotional reactivity. As well as behavioural data and ECG collected during behavioural tasks.

Participants rated the arousal of 48 affective images paired with semantically matching sounds. Heart rate deceleration was used as a measure of their autonomic arousal. Half of these sounds were cued during REM in the subsequent overnight sleep cycle. Participants rated the images in an MRI scanner with pulse oximetry 48 hours after encoding, and they completed an online follow up two weeks later.

Disarming emotional memories using Targeted Memory Reactivation during Rapid Eye Movement sleep

Sessions

Baseline: Initial arousal ratings as well as overnight sleep with TMR
Session 48-H: fMRI scanning, pulse oximetry and arousal ratings (48 hours after baseline)
Session 2-Wk: Online follow-up (2 weeks after baseline)

Data Acquisition

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=17, range 21–32 yr, mean 25.1 yr)

202530

Female · 10Male · 7

Sex composition

Female
11
Male
7
F : M ratio
1.57 : 1

61% female · n = 18 subjects with reported sex.

Channel counts: 10 ch (n=21 recordings)

Sampling frequencies: 500.0 Hz (n=21 recordings)

Total recording duration: 144 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 10 ch · EEG · 500 Hz · 17 subjects, 21 recordings

Live trace viewer — sub-12012022301 · task-sleep · run-02

Showing one representative recording out of 17 subjects and 21 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

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS005530(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Greco2024

Canonical—

Importable asDS005530 · Greco2024

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS005530(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Depotentiation of emotional reactivity using TMR during REM sleep

Study:: ds005530 (OpenNeuro)
Author (year):: Greco2024
Canonical:: —

Also importable as: DS005530, Greco2024.

Modality: eeg; Experiment type: Sleep; Subject type: Healthy. Subjects: 17; recordings: 21; 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/ds005530 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005530 DOI: https://doi.org/10.18112/openneuro.ds005530.v1.0.9 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005530
>>> dataset = DS005530(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/ds005530 · pull with datasets.load_dataset("EEGDash/ds005530").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005530.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 ds005530 to reproduce the tutorial on this dataset.

Citation

Viviana Greco, Tamas A. Foldes, Neil A. Harrison, Kevin Murphy, Marta Wawrzuta, … (n.d.). Depotentiation of emotional reactivity using TMR during REM sleep. 10.18112/openneuro.ds005530.v1.0.9

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005530.v1.0.9.

Related & sibling datasets

DS007406EEG · 10 subj DS004661EEG · 17 subj DS007006EEG · 10 subj DS005815EEG · 20 subj DS006394EEG · 33 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds005530.v1.0.9

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005530`
Title	Depotentiation of emotional reactivity using TMR during REM sleep
Author (year)	`Greco2024`
Canonical	—
Importable as	`DS005530`, `Greco2024`
Year	—
Authors	Viviana Greco, Tamas A. Foldes, Neil A. Harrison, Kevin Murphy, Marta Wawrzuta, Mahmoud E. A. Abdellahi, Penelope A. Lewis
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005530.v1.0.9
Source links	OpenNeuro \| NeMAR \| Source URL

DS005530: eeg dataset, 17 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#