DS003574#

Reward biases spontaneous neural reactivation during sleep

Access recordings and metadata through EEGDash.

Citation: Virginie Sterpenich, Mojca KM van Schie, Maximilien Catsiyannis, Avinash Ramyead, Stephen Perrig, Hee-Deok Yang, Dimitri Van De Ville, Sophie Schwartz (2021). Reward biases spontaneous neural reactivation during sleep. 10.18112/openneuro.ds003574.v1.0.2

Modality: eeg Subjects: 18 Recordings: 315 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS003574`
Title	Reward biases spontaneous neural reactivation during sleep
Year	2021
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	10.18112/openneuro.ds003574.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: 18
Recordings: 315
Tasks: 4

Channels & sampling rate

Channels: 64 (18), 69 (18)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS003574 class to access this dataset programmatically.

class eegdash.dataset.DS003574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003574. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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/ds003574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003574

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, 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