DS003768#

Simultaneous EEG and fMRI signals during sleep from humans

Access recordings and metadata through EEGDash.

Citation: Yameng Gu, Feng Han, Lucas E. Sainburg, Margeaux M. Schade, Xiao Liu (2021). Simultaneous EEG and fMRI signals during sleep from humans. 10.18112/openneuro.ds003768.v1.0.13

Modality: eeg Subjects: 33 Recordings: 1346 License: CC0 Source: openneuro Citations: 21.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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 Xiao Liu},
  doi = {10.18112/openneuro.ds003768.v1.0.13},
  url = {https://doi.org/10.18112/openneuro.ds003768.v1.0.13},
}

About This Dataset#

This dataset included 33 healthy participants collected at Pennsylvania State University with informed consent. Simultaneously collected EEG and BOLD signals for each participant were recorded and organized at each folder.

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. In the TSV file, “w” represents wakefulness and “1, 2, 3” represents NREM1, NREM2, and NREM3, respectively. Some epochs scoring with uncertainty are noted as “uncertain” and some epochs with too large artifacts to score reasonably are noted as “unscorable”.

MR imaging data were collected on a 3 Tesla Prisma Siemens Fit scanner using a Siemens 20-channel receive-array coil. Anatomical images were acquired using a MPRAGE sequence (TR: 2300 milliseconds, TE: 2.28 milliseconds, 1mm isotropic spatial resolution, FOV: 256 millimeters, flip angle: 8 degrees, matrix size: 256×256×192, acceleration factor: 2). Blood oxygenation level-dependent (BOLD) fMRI data were acquired using an EPI sequence (TR: 2100 milliseconds, TE: 25 milliseconds, slice thickness: 4mm, slices: 35, FOV: 240mm, in-plane resolution: 3mm×3mm).

EEG data were collected using a 32-channel MR-compatible EEG system from Brain Products, Germany. Electrodes were placed based on the 10-20 international system. EOG and ECG recorded eye movement and cardiac signal, respectively. EEG data were collected at a sampling rate of 5000 Hz with a band-pass filter of 0-250 Hz. R128 in the EEG signals corresponds to the BOLD fMRI volume trigger. S1 markers in the EEG during sleep sessions correspond to participants hitting buttons indicating wakefulness state. S2 and S3 markers during sleep sessions represent no button hitting and can be ignored.

For more information or any questions about this dataset, please see the two papers listed in the References and Links section or contact Dr. Yameng Gu (ymgu95@gmail.com)

Dataset Information#

Dataset ID	`DS003768`
Title	Simultaneous EEG and fMRI signals during sleep from humans
Year	2021
Authors	Yameng Gu, Feng Han, Lucas E. Sainburg, Margeaux M. Schade, Xiao Liu
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003768.v1.0.13
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: 33
Recordings: 1346
Tasks: 2

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 5000.0
Duration (hours): 0.0

API Reference#

Use the DS003768 class to access this dataset programmatically.

class eegdash.dataset.DS003768(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003768. Modality: eeg; Experiment type: Sleep; Subject type: Healthy. 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

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/ds003768 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003768

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, 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