DS005448: ieeg dataset, 13 subjects#
STReEF
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. (20). STReEF. 10.18112/openneuro.ds005448.v1.0.0
13-participant iEEG dataset — STReEF.
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: 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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=13, range 10–50 yr, mean 28.2 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 2048.0 Hz (n=18 recordings)
Total recording duration: 12 h 22 min
Signal · Electrodes & live trace#
Live trace viewer — sub-STREEF03 · ses-1 · task-SPESclin · run-021318
Showing one representative recording out of
13 subjects and 18 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _ieeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?ieeg=<url>) to inspect it.
Electrode layout — iEEG · 103 sensors — 103 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
STReEF |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
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 |
|
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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005448 · Jelsma2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005448(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
STReEF
- Study:
ds005448(OpenNeuro)- Author (year):
Jelsma2024- Canonical:
—
Also importable as:
DS005448,Jelsma2024.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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds005448").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds005448 to reproduce the tutorial on this dataset.
Citation
Jelsma S.B., Zijlmans M., Heijink I.B., Hoefnagels F.W.A., Raemakers M, … (20). STReEF. 10.18112/openneuro.ds005448.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005448.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset