DS005448: ieeg dataset, 13 subjects#

STReEF

Access recordings and metadata through EEGDash.

Citation: Jelsma S.B., Zijlmans M., Heijink I.B., Hoefnagels F.W.A., Raemakers M, Bourez-Swart M.D., Otte W.M, van Blooijs D., van Klink N.E.C. (2024). STReEF. 10.18112/openneuro.ds005448.v1.0.0

Modality: ieeg Subjects: 13 Recordings: 18 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005448

dataset = DS005448(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005448(cache_dir="./data", subject="01")

Advanced query

dataset = DS005448(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds005448,
  title = {STReEF},
  author = {Jelsma S.B. and Zijlmans M. and Heijink I.B. and Hoefnagels F.W.A. and Raemakers M and Bourez-Swart M.D. and Otte W.M and van Blooijs D. and van Klink N.E.C.},
  doi = {10.18112/openneuro.ds005448.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005448.v1.0.0},
}

About This Dataset#

Dataset description This dataset is part of a bigger dataset of intracranial EEG (iEEG) called RESPect (Registry for Epilepsy Surgery Patients), a dataset recorded at the University Medical Center of Utrecht, the Netherlands. This dataset consists of 13 patients with long-term recordings (5 patients recorded with electrocorticography and 8 patients recorded with stereo-encephalography. For a detailed description see Jelsma S.B. et al 2024, Structural and effective brain connectivity in focal epilepsy. This data is organized according to the Brain Imaging Data Structure specification: A community-driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/ Each patient has their own folder (e.g., sub-STREEF01) which contains the iEEG recordings of that patient, as well as the metadata to understand the raw data and event timing. In long-term recordings, data that are recorded within one monitoring period are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording in the monitoring period. We use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15). The task key-value pair in long-term iEEG recordings describes the patient´s state during the recording of this file. A specific task called “SPESclin“ is defined when the clinical SPES protocol has been performed. License This dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at https://creativecommons.org/publicdomain/zero/1.0/. We hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/). In particular, while not legally required, we hope that all users of the data will acknowledge by citing: 1. Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS“, published in NeuroInformatics in 2022 2. Jelsma S.B. et al 2024, Structural and effective brain connectivity in focal epilepsy

in any publications. Code available at: https://github.com/UMCU-EpiLAB/umcuEpi_CCEP_DTI. Acknowledgements We thank the SEIN-UMCU RESPect database group (C.J.J. van Asch, L. van de Berg, S. Blok, M.D. Bourez, K.P.J. Braun, J.W. Dankbaar, C.H. Ferrier, T.A. Gebbink, P.H. Gosselaar, R. van Griethuysen, M.G.G. Hobbelink, F.W.A. Hoefnagels, N.E.C. van Klink, M.A. van ‘t Klooster, G.A.P. deKort, M.H.M. Mantione, A. Muhlebner, J.M. Ophorst, P.C. van Rijen, S.M.A. van der Salm, E.V. Schaft, M.M.J. van Schooneveld, H. Smeding, D. Sun, A. Velders, M.J.E. van Zandvoort, G.J.M. Zijlmans, E. Zuidhoek and J. Zwemmer) for their contributions and help in collecting the data.

Dataset Information#

Dataset ID

DS005448

Title

STReEF

Author (year)

Jelsma2024

Canonical

STReEF

Importable as

DS005448, Jelsma2024, STReEF

Year

2024

Authors

Jelsma S.B., Zijlmans M., Heijink I.B., Hoefnagels F.W.A., Raemakers M, Bourez-Swart M.D., Otte W.M, van Blooijs D., van Klink N.E.C.

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005448.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005448,
  title = {STReEF},
  author = {Jelsma S.B. and Zijlmans M. and Heijink I.B. and Hoefnagels F.W.A. and Raemakers M and Bourez-Swart M.D. and Otte W.M and van Blooijs D. and van Klink N.E.C.},
  doi = {10.18112/openneuro.ds005448.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005448.v1.0.0},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 13

  • Recordings: 18

  • Tasks: 1

Channels & sampling rate

  • Channels: 133 (14), 109 (2), 95, 161

  • Sampling rate (Hz): 2048.0

  • Duration (hours): 12.382256944444444

Tags

  • Pathology: Epilepsy

  • Modality: Other

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 44.7 GB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005448.v1.0.0

Provenance

API Reference#

Use the DS005448 class to access this dataset programmatically.

class eegdash.dataset.DS005448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

STReEF

Study:

ds005448 (OpenNeuro)

Author (year):

Jelsma2024

Canonical:

STReEF

Also importable as: DS005448, Jelsma2024, STReEF.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 13; recordings: 18; 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/ds005448 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005448 DOI: https://doi.org/10.18112/openneuro.ds005448.v1.0.0

Examples

>>> from eegdash.dataset import DS005448
>>> dataset = DS005448(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#