eegdash.dataset.DS001785#

task-thrup_events.json (OpenNeuro ds001785). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS001785

Title

task-thrup_events.json

Year

2019

Authors

Michael Pereira, Pierre Mégevand, Mi Xue Tan, Wenwen Chang, Shuo Wang, Ali Rezai, Margitta Seeck, Marco Corniola, Shahan Momjian, Fosco Bernasconi, Olaf Blanke, Nathan Faivre

License

CC0

Citation / DOI

10.18112/openneuro.ds001785.v1.1.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds001785,
  title = {task-thrup_events.json},
  author = {Michael Pereira and Pierre Mégevand and Mi Xue Tan and Wenwen Chang and Shuo Wang and Ali Rezai and Margitta Seeck and Marco Corniola and Shahan Momjian and Fosco Bernasconi and Olaf Blanke and Nathan Faivre},
  doi = {10.18112/openneuro.ds001785.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds001785.v1.1.1},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 71

  • 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: 10.18112/openneuro.ds001785.v1.1.1

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS001785

dataset = DS001785(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS001785(cache_dir="./data", subject="01")

dataset = DS001785(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

class eegdash.dataset.DS001785(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds001785. Modality: eeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 18; recordings: 54; 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/ds001785 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001785 DOI: https://doi.org/10.18112/openneuro.ds001785.v1.1.1

Examples

>>> from eegdash.dataset import DS001785
>>> dataset = DS001785(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#