eegdash.dataset.DS003708#

Basis profile curve identification to understand electrical stimulation effects in human brain networks (OpenNeuro ds003708). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS003708

Title

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Year

Unknown

Authors

Dora Hermes, Gabriella Ojeda, Kai J. Miller, Multimodal Neuroimaging Laboratory at Mayo Clinic

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003708.v1.0.4

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003708,
  title = {Basis profile curve identification to understand electrical stimulation effects in human brain networks},
  author = {Dora Hermes and Gabriella Ojeda and Kai J. Miller and Multimodal Neuroimaging Laboratory at Mayo Clinic},
  doi = {10.18112/openneuro.ds003708.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds003708.v1.0.4},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: Unknown

  • 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.ds003708.v1.0.4

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS003708

dataset = DS003708(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS003708(cache_dir="./data", subject="01")

dataset = DS003708(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

class eegdash.dataset.DS003708(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003708. Modality: ieeg; Experiment type: Unknown; Subject type: connectivity. Subjects: 2; recordings: 281; 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/ds003708 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003708 DOI: https://doi.org/10.18112/openneuro.ds003708.v1.0.4

Examples

>>> from eegdash.dataset import DS003708
>>> dataset = DS003708(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#