DS003768: eeg dataset, 33 subjects#
Simultaneous EEG and fMRI signals during sleep from humans
Citation: Yameng Gu, Feng Han, Lucas E. Sainburg, Margeaux M. Schade, Orfeu M. Buxton, Jeff H. Duyn, Xiao Liu (20). Simultaneous EEG and fMRI signals during sleep from humans. 10.18112/openneuro.ds003768.v1.0.0
33-participant EEG dataset — Simultaneous EEG and fMRI signals during sleep from humans.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003768
dataset = DS003768(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003768(cache_dir="./data", subject="01")
Advanced query
dataset = DS003768(
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{ds003768,
title = {Simultaneous EEG and fMRI signals during sleep from humans},
author = {Yameng Gu and Feng Han and Lucas E. Sainburg and Margeaux M. Schade and Orfeu M. Buxton and Jeff H. Duyn and Xiao Liu},
doi = {10.18112/openneuro.ds003768.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003768.v1.0.0},
}
About This Dataset#
This dataset included 33 healthy participants collected at Penn State with informed consent. Simultaneously collected EEG and BOLD signals for each participant were recorded and organized at each folder. EEG data were collected using a 32 channel MR-compatible EEG system (Brain Products, Munich, Germany). R128 in the EEG signals corresponds to the BOLD fMRI volume trigger.
Each scanning section consisted of an anatomical session, two 10-min resting-state sessions, and several 15-min sleep sessions. The first resting-state session was conducted before a visual-motor adaptation task (Albouy et al, Journal of Sleep Research, 2013) and the second resting-state session was conducted after a visual-motor adaptation task. The scored sleep stages for these 33 subjects were organized under sourcedata folder. Each TSV file contained the sleep stages for each 30-sec epoch across different sessions for each subject.
For more information or any questions about this dataset, please see the manuscript on bioRxiv or contact Yameng Gu (ymgu95@gmail.com)
Cohort#
Dataset Statistics#
Channel counts: 32 ch (n=255 recordings)
Sampling frequencies: 5000.0 Hz (n=255 recordings)
Total recording duration: 59 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-sleep · run-2
Showing one representative recording out of
33 subjects and 255 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
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 |
Simultaneous EEG and fMRI signals during sleep from humans |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Yameng Gu, Feng Han, Lucas E. Sainburg, Margeaux M. Schade, Orfeu M. Buxton, Jeff H. Duyn, Xiao Liu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003768,
title = {Simultaneous EEG and fMRI signals during sleep from humans},
author = {Yameng Gu and Feng Han and Lucas E. Sainburg and Margeaux M. Schade and Orfeu M. Buxton and Jeff H. Duyn and Xiao Liu},
doi = {10.18112/openneuro.ds003768.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003768.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003768 · Gu2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Simultaneous EEG and fMRI signals during sleep from humans
- Study:
ds003768(OpenNeuro)- Author (year):
Gu2021- Canonical:
—
Also importable as:
DS003768,Gu2021.Modality:
eeg. Subjects: 33; recordings: 255; 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/ds003768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003768 DOI: https://doi.org/10.18112/openneuro.ds003768.v1.0.0 NEMAR citation count: 21
Examples
>>> from eegdash.dataset import DS003768 >>> dataset = DS003768(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/ds003768").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 ds003768 to reproduce the tutorial on this dataset.
Citation
Yameng Gu, Feng Han, Lucas E. Sainburg, Margeaux M. Schade, Orfeu M. Buxton, … (20). Simultaneous EEG and fMRI signals during sleep from humans. 10.18112/openneuro.ds003768.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003768.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset