EEGdash›OpenNeuro›DS005620

Iss. 5620 · 21 subjects · 202 recordings · CC0

Dataset Brief · A repeated awakening study exploring the capacity of complexi…

DS005620: eeg dataset, 21 subjects#

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

Citation: Imad J. Bajwa1, Andre S. Nilsen1, René Skukies1,3, Arnfinn Aamodt1, Gernot Ernst2, Johan F. Storm1, Bjørn E. Juel1,2 (2019). A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation. 10.18112/openneuro.ds005620.v1.0.0

21-participant EEG dataset — A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation.

Data & curation Imad J. Bajwa1 · Andre S. Nilsen1 · René Skukies1,3 · Arnfinn Aamodt1 · Gernot Ernst2 · Johan F. Storm1 · …
Year 2019 · Distributed via OpenNeuro

EEG · 64 (132), 65 (70) ch5000 HzBIDS 1.7.03 tasksHealthyAnesthesiaClinical/Intervention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

About This Dataset#

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.

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

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

Description

License

This dataset is licensed under CC-BY-4.0.

File Structure

View full README

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

Description

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=17, range 19–30 yr, mean 25.3 yr)

15202530

Female · 10Male · 7

Sex composition

Female
12
Male
8
F : M ratio
1.50 : 1

60% female · n = 20 subjects with reported sex.

Channel counts (ch)

6465

Sampling frequencies: 5000.0 Hz (n=202 recordings)

Total recording duration: 17 h 56 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 (132), 65 (70) ch · EEG · 5000 Hz · 21 subjects, 202 recordings

Live trace viewer — sub-1022 · task-sed2 · run-3

Showing one representative recording out of 21 subjects and 202 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 · 62 sensors — 62 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS005620(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Bajwa2024

Canonical—

Importable asDS005620 · Bajwa2024

Sourceeegdash/dataset/registry.py · [source ↗]

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

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

Study:: ds005620 (OpenNeuro)
Author (year):: Bajwa2024
Canonical:: —

Also importable as: DS005620, Bajwa2024.

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 DOI: https://doi.org/10.18112/openneuro.ds005620.v1.0.0 NEMAR citation count: 0

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: 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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds005620 · pull with datasets.load_dataset("EEGDash/ds005620").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005620.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds005620 to reproduce the tutorial on this dataset.

Citation

Imad J. Bajwa1, Andre S. Nilsen1, René Skukies1,3, Arnfinn Aamodt1, Gernot Ernst2, … (2019). A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation. 10.18112/openneuro.ds005620.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.ds005620.v1.0.0.

Related & sibling datasets

DS003380EEG · 1 subj DS005779EEG · 19 subj DS003670EEG · 25 subj DS006437EEG · 9 subj DS005688EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds005620.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005620`
Title	A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation
Author (year)	`Bajwa2024`
Canonical	—
Importable as	`DS005620`, `Bajwa2024`
Year	2019
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

DS005620: eeg dataset, 21 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#