DS005620#

A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation

Access recordings and metadata through EEGDash.

Citation: Imad J. Bajwa1, Andre S. Nilsen1, René Skukies1,3, Arnfinn Aamodt1, Gernot Ernst2, Johan F. Storm1, Bjørn E. Juel1,2 (2024). A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation. 10.18112/openneuro.ds005620.v1.0.0

Modality: eeg Subjects: 21 Recordings: 1440 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005620

dataset = DS005620(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 = DS005620(cache_dir="./data", subject="01")

Advanced query

dataset = DS005620(
    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{ds005620,
  title = {A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation},
  author = {Imad J. Bajwa1 and Andre S. Nilsen1 and René Skukies1,3 and Arnfinn Aamodt1 and Gernot Ernst2 and Johan F. Storm1 and Bjørn E. Juel1,2},
  doi = {10.18112/openneuro.ds005620.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005620.v1.0.0},
}

About This Dataset#

A Repeated Awakening Study Exploring the Capacity of Complexity Measures to Capture Dreaming During Propofol Sedation

Description

This dataset contains EEG data from a study investigating the effects of propofol sedation on dreaming and the applicability of complexity measures in capturing this phenomenon. The study aims to understand the dynamics of consciousness during sedation and the potential for EEG complexity measures to reflect subjective experiences.

View full README

A Repeated Awakening Study Exploring the Capacity of Complexity Measures to Capture Dreaming During Propofol Sedation

Description

This dataset contains EEG data from a study investigating the effects of propofol sedation on dreaming and the applicability of complexity measures in capturing this phenomenon. The study aims to understand the dynamics of consciousness during sedation and the potential for EEG complexity measures to reflect subjective experiences.

Authors

Imad J. Bajwa
Andre S. Nilsen
René Skukies
Arnfinn Aamodt
Gernot Ernst
Johan F. Storm
Bjørn E. Juel

Ethics Statement

Approved by the Regional Committees for Medical Research Ethics South East Norway (REK), ref. 2015/1520.

License

This dataset is licensed under CC-BY-4.0.

File Structure

The dataset is organized by subject, with each subject’s EEG files stored in a dedicated directory. Below is the structure for the EEG data associated with a sample subject (sub-1016).

Directory Structure

/Volumes/IMADS SSD/Anesthesia_conciousness_paper/project_BIDS/

└── sub-1016/
    └── eeg/
        ├── sub-1016_task-awake_acq-EC_channels.tsv
        ├── sub-1016_task-awake_acq-EC_eeg.eeg
        ├── sub-1016_task-awake_acq-EC_eeg.json
        ├── sub-1016_task-awake_acq-EC_eeg.vhdr
        ├── sub-1016_task-awake_acq-EC_eeg.vmrk
        ├── sub-1016_task-awake_acq-EC_events.json
        ├── sub-1016_task-awake_acq-EC_events.tsv
        ├── ...

File Naming Convention

EEG files are named in the format: sub-<subject_id>_task-<task_name>_acq-<acquisition>_run-<run_number>.<extension>

Example Filenames

sub-1016_task-awake_acq-EC_channels.tsv
sub-1016_task-sed_acq-rest_run-1_eeg.eeg

Filename Components

sub-<subject_id>: Identifier for the subject (e.g., sub-1016).
task-<task_name>: Indicates the task condition: - awake: Wakefulness - sed: Sedation condition - sed2: One-minute resting EEG recorded just before an awakening
acq-<acquisition>: Type of acquisition: - EC: Eyes Closed (during wakefulness) - EO: Eyes Open (during wakefulness) - tms: Session with Transcranial Magnetic Stimulation - rest: Rest condition (during sedation)
run-<run_number>: Specifies the run number for the data collection: - run-1, run-2, run-3 (indicating different awakenings in sedation)
**.<extension>**: File extension indicating the type of file (e.g., .eeg, .vhdr, .vmrk, etc.).

File Types

**.eeg**: Raw EEG data.
**.vhdr**: BrainVision header file.
**.vmrk**: BrainVision marker file.
**_events.json / _events.tsv**: Event markers.
**_channels.tsv / _eeg.json**: Channel information and metadata.

Usage Instructions

To analyze the data, you may need software such as Python with MNE-Python. Please refer to the MNE documentation for details on how to load and manipulate the datasets.

Contact Information

For questions regarding this dataset, please contact: Imad J. Bajwa Email: imadjb@uio.no

Bjørn E. Juel Email: Bjorneju@gmail.com

Acknowledgements

We thank the participants and the supporting research staff for their contributions to this study.

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

Dataset Information#

Dataset ID	`DS005620`
Title	A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation
Year	2024
Authors	Imad J. Bajwa1, Andre S. Nilsen1, René Skukies1,3, Arnfinn Aamodt1, Gernot Ernst2, Johan F. Storm1, Bjørn E. Juel1,2
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005620.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: 21
Recordings: 1440
Tasks: 3

Channels & sampling rate

Channels: 64 (264), 65 (140)
Sampling rate (Hz): 5000.0
Duration (hours): 0.0

API Reference#

Use the DS005620 class to access this dataset programmatically.

class eegdash.dataset.DS005620(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005620. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 21; recordings: 202; tasks: 3.

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/ds005620 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005620

Examples

>>> from eegdash.dataset import DS005620
>>> dataset = DS005620(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