DS003768: eeg dataset, 33 subjects#

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, Orfeu M. Buxton, Jeff H. Duyn, Xiao Liu (2021). Simultaneous EEG and fMRI signals during sleep from humans. 10.18112/openneuro.ds003768.v1.0.0

Modality: eeg Subjects: 33 Recordings: 255 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 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)

Dataset Information#

Dataset ID	`DS003768`
Title	Simultaneous EEG and fMRI signals during sleep from humans
Author (year)	`Gu2021`
Canonical	—
Importable as	`DS003768`, `Gu2021`
Year	2021
Authors	Yameng Gu, Feng Han, Lucas E. Sainburg, Margeaux M. Schade, Orfeu M. Buxton, Jeff H. Duyn, Xiao Liu
License	CC0
Citation / DOI	10.18112/openneuro.ds003768.v1.0.0
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: 255
Tasks: 2

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 5000.0
Duration (hours): Not calculated

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Channel counts: 32 ch (n=255 recordings)

Sampling frequencies: 5000.0 Hz (n=255 recordings)

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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

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

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 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)