ON004902: eeg dataset, 71 subjects#
A Resting-state EEG Dataset for Sleep Deprivation
Access recordings and metadata through EEGDash.
Citation: Chuqin Xiang, Xinrui Fan, Duo Bai, Ke Lv, Xu Lei (2024). A Resting-state EEG Dataset for Sleep Deprivation. 10.82901/nemar.on004902
Modality: eeg Subjects: 71 Recordings: 218 License: CC0 Source: nemar
Metadata: Complete (100%)
71-participant EEG dataset — A Resting-state EEG Dataset for Sleep Deprivation.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON004902
dataset = ON004902(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON004902(cache_dir="./data", subject="01")
Advanced query
dataset = ON004902(
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{on004902,
title = {A Resting-state EEG Dataset for Sleep Deprivation},
author = {Chuqin Xiang and Xinrui Fan and Duo Bai and Ke Lv and Xu Lei},
doi = {10.82901/nemar.on004902},
url = {https://doi.org/10.82901/nemar.on004902},
}
About This Dataset#
The dataset provides resting-state EEG data (eyes open,partially eyes closed) from 71 participants who underwent two experiments involving normal sleep (NS—session1) and sleep deprivation(SD—session2) .The dataset also provides information on participants’ sleepiness and mood states.
（Please note here Session 1 (NS) and Session 2 (SD) is not the time order, the time order is counterbalanced across participants and is listed in metadata.）
The data collection was initiated in March 2019 and was terminated in December 2020. The detailed description of the dataset is currently under working by Chuqin Xiang,Xinrui Fan,Duo Bai,Ke Lv and Xu Lei, and will submit to Scientific Data for publication.
General information
EEG acquisition
* EEG system (Brain Products GmbH, Steing- rabenstr, Germany, 61 electrodes) * Sampling frequency: 500Hz * Impedances were kept below 5k
Contact
If you have any questions or comments, please contact:
Xu Lei: xlei@swu.edu.cn
Article
Xiang, C., Fan, X., Bai, D. et al. A resting-state EEG dataset for sleep deprivation. Sci Data 11, 427 (2024). https://doi.org/10.1038/s41597-024-03268-2
Cohort#
Dataset Statistics#
Age distribution (n=71, range 17–23 yr, mean 20.0 yr · sex per subject not reported)
Sex composition
Channel counts: 61 ch (n=218 recordings)
Sampling frequencies (Hz)
Total recording duration: 18 h 7 min
Signal · Electrodes & live trace#
Live trace viewer — sub-17 · ses-1 · task-eyesclosed
Showing one representative recording out of
71 subjects and 218 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 · 61 sensors — 61 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 |
A Resting-state EEG Dataset for Sleep Deprivation |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Chuqin Xiang, Xinrui Fan, Duo Bai, Ke Lv, Xu Lei |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on004902,
title = {A Resting-state EEG Dataset for Sleep Deprivation},
author = {Chuqin Xiang and Xinrui Fan and Duo Bai and Ke Lv and Xu Lei},
doi = {10.82901/nemar.on004902},
url = {https://doi.org/10.82901/nemar.on004902},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON004902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
A Resting-state EEG Dataset for Sleep Deprivation
- Study:
on004902(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON004902.Modality:
eeg; Subject type:Unknown. Subjects: 71; recordings: 218; tasks: 2.- 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/on004902 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on004902 DOI: https://doi.org/10.82901/nemar.on004902
Examples
>>> from eegdash.dataset import ON004902 >>> dataset = ON004902(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.pytorch
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 on004902 to reproduce the tutorial on this dataset.
Citation
Chuqin Xiang, Xinrui Fan, Duo Bai, Ke Lv, Xu Lei (2024). A Resting-state EEG Dataset for Sleep Deprivation. 10.82901/nemar.on004902
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on004902.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset