eegdash.dataset.DS006366#

task-sleep_events.json (OpenNeuro ds006366). Access recordings and metadata through EEGDash.

Modality: [‘eeg’] Tasks: 0 License: CC0 Subjects: 0 Recordings: 0 Source: openneuro

Dataset Information#

Dataset ID

DS006366

Title

task-sleep_events.json

Year

2025

Authors

Laura Rose, Alexander Neergaard Zahid, Javier García Ciudad, Christine Egebjerg, Louise Piilgaard, Frederikke Lynge Sørensen, Mie Andersen, Tessa Radovanovic, Anastasia Tsopanidou, Maiken Nedergaard, Sébastien Arthaud, Renato Maciel, Christelle Peyron, Chiara Berteotti, Viviana Lo Martire, Alessandro Silvani, Giovanna Zoccoli, Micaela Borsa, Antoine Adamantidis, Morten Mørup, Birgitte Rahbek Kornum

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006366.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006366,
  title = {task-sleep_events.json},
  author = {Laura Rose and Alexander Neergaard Zahid and Javier García Ciudad and Christine Egebjerg and Louise Piilgaard and Frederikke Lynge Sørensen and Mie Andersen and Tessa Radovanovic and Anastasia Tsopanidou and Maiken Nedergaard and Sébastien Arthaud and Renato Maciel and Christelle Peyron and Chiara Berteotti and Viviana Lo Martire and Alessandro Silvani and Giovanna Zoccoli and Micaela Borsa and Antoine Adamantidis and Morten Mørup and Birgitte Rahbek Kornum},
  doi = {10.18112/openneuro.ds006366.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006366.v1.0.1},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 2 (43), 3 (71), 5 (34)

  • Sampling rate (Hz): Unknown

  • Duration (hours): 0

Tasks & conditions

  • Tasks: 0

  • Experiment type: Unknown

  • Subject type: Unknown

Files & format

  • Size on disk: Unknown

  • File count: Unknown

  • Format: Unknown

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006366.v1.0.1

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS006366

dataset = DS006366(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS006366(cache_dir="./data", subject="01")

dataset = DS006366(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

  • No dataset-specific caveats are listed in the available metadata.

API#

class eegdash.dataset.DS006366(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006366. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 92; recordings: 148; tasks: 1.

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/ds006366 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006366 DOI: https://doi.org/10.18112/openneuro.ds006366.v1.0.1

Examples

>>> from eegdash.dataset import DS006366
>>> dataset = DS006366(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

See Also#