DS003574: eeg dataset, 18 subjects#
Reward biases spontaneous neural reactivation during sleep
Citation: Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, Hee-Deok Yang, Dimitri Van De Ville, Sophie Schwartz (—). Reward biases spontaneous neural reactivation during sleep. 10.18112/openneuro.ds003574.v1.0.2
18-participant EEG dataset — Reward biases spontaneous neural reactivation during sleep.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003574
dataset = DS003574(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003574(cache_dir="./data", subject="01")
Advanced query
dataset = DS003574(
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{ds003574,
title = {Reward biases spontaneous neural reactivation during sleep},
author = {Virginie Sterpenich and Mojca KM van Schie and Maximilien Catsiyannis and Avinash Ramyead and Stephen Perrig and Hee-Deok Yang and Dimitri Van De Ville and Sophie Schwartz},
doi = {10.18112/openneuro.ds003574.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003574.v1.0.2},
}
About This Dataset#
The data included 18 participants that played at 2 different games during wakefulness in the 3T MRI: the FACE and the MAZE game, intermixed with periods of REST and period of preparation of each game (game session). The tasks were manipulated and at the end of the game session, one game was won (Reward game) and the second was lost (No Reward game), randomly assigned for each participant. Next, during the sleep session, 64 electrodes were placed on the head of the participants, before they slept in the MRI with EEG for 1-2 hours (sleep session). Participants can be separated according to the won game (face or maze) and according sleep depth (whether they reached N3 sleep in the MRI or only N2 sleep). A decoding classifier was trained on the data from the game session at wake and applied to the MRI data acquired during sleep (sleep session). Finally, a memory test was performed the next day on the 2 tasks (face and maze).
For any question related to the methods, please see the manuscript or contact Virginie Sterpenich (Virginie.Sterpenich@unige.ch)
Files includes are 1) 2 EPI sessions for the tasks 2) 1 EPI session during resting including wake and sleep (sleep session) 3) 1 EEG file corresponding to the sleep session (including wake and sleep in the MRI) 4) 1 T1 anatomical image
Cohort#
Dataset Statistics#
Age distribution by gender (n=18, range 18–26 yr, mean 22.1 yr)
Sex composition
Channel counts: 69 ch (n=18 recordings)
Sampling frequencies: 500.0 Hz (n=18 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-rest
Showing one representative recording out of
18 subjects and 18 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 · 63 sensors — 63 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.
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 |
Reward biases spontaneous neural reactivation during sleep |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, Hee-Deok Yang, Dimitri Van De Ville, Sophie Schwartz |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003574,
title = {Reward biases spontaneous neural reactivation during sleep},
author = {Virginie Sterpenich and Mojca KM van Schie and Maximilien Catsiyannis and Avinash Ramyead and Stephen Perrig and Hee-Deok Yang and Dimitri Van De Ville and Sophie Schwartz},
doi = {10.18112/openneuro.ds003574.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003574.v1.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003574 · Sterpenich2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Reward biases spontaneous neural reactivation during sleep
- Study:
ds003574(OpenNeuro)- Author (year):
Sterpenich2021- Canonical:
—
Also importable as:
DS003574,Sterpenich2021.Modality:
eeg. Subjects: 18; recordings: 18; 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/ds003574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003574 DOI: https://doi.org/10.18112/openneuro.ds003574.v1.0.2 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS003574 >>> dataset = DS003574(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/ds003574").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 ds003574 to reproduce the tutorial on this dataset.
Citation
Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, … (n.d.). Reward biases spontaneous neural reactivation during sleep. 10.18112/openneuro.ds003574.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003574.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset