eegdash.dataset.DS005795#

scans.json (OpenNeuro ds005795). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS005795

Title

scans.json

Year

2025

Authors

Jörg Stadler, Torsten Stöter, Nicole Angenstein, Andreas Fügner, Marcel Lommerzheim, Artur Mathysiak, Anke Michalsky, Gabriele Schöps, Johann van der Meer, Susann Wolff, André Brechmann

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005795.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005795,
  title = {scans.json},
  author = {Jörg Stadler and Torsten Stöter and Nicole Angenstein and Andreas Fügner and Marcel Lommerzheim and Artur Mathysiak and Anke Michalsky and Gabriele Schöps and Johann van der Meer and Susann Wolff and André Brechmann},
  doi = {10.18112/openneuro.ds005795.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005795.v1.0.0},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 72

  • Sampling rate (Hz): 500.0

  • 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.ds005795.v1.0.0

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS005795

dataset = DS005795(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS005795(cache_dir="./data", subject="01")

dataset = DS005795(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

class eegdash.dataset.DS005795(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005795. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 34; recordings: 39; 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/ds005795 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005795 DOI: https://doi.org/10.18112/openneuro.ds005795.v1.0.0

Examples

>>> from eegdash.dataset import DS005795
>>> dataset = DS005795(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#