DS003876: ieeg dataset, 39 subjects#
Epilepsy-iEEG-Interictal-Multicenter-Dataset
Citation: Gunnarsdottir, Kristin, Li, Adam, Smith, Rachel, Kang, Joon, Korzeniewska, Anna, Crone, Nathan, Rouse, Adam, Cheng, Jennifer, Kinsman, Michael, Landazuri, Patrick, Uysal, Utku, Ulloa, Carol, Cameron, Nathaniel, Cajigas, Iahn, Jagid, Jonathan, Kanner, Andres, Elarjani, Turki, Bicchi, Manuel, Inati, Sara, Zaghloul, Kareem, Boerwinkle, Varina, Wyckoff, Sarah, Barot, Niravkumar, Gonzalez-Martinez, Jorge, Sarma, Sridevi (2021). Epilepsy-iEEG-Interictal-Multicenter-Dataset. 10.18112/openneuro.ds003876.v1.0.2
39-participant iEEG dataset — Epilepsy-iEEG-Interictal-Multicenter-Dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003876
dataset = DS003876(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003876(cache_dir="./data", subject="01")
Advanced query
dataset = DS003876(
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{ds003876,
title = {Epilepsy-iEEG-Interictal-Multicenter-Dataset},
author = {Gunnarsdottir, Kristin and Li, Adam and Smith, Rachel and Kang, Joon and Korzeniewska, Anna and Crone, Nathan and Rouse, Adam and Cheng, Jennifer and Kinsman, Michael and Landazuri, Patrick and Uysal, Utku and Ulloa, Carol and Cameron, Nathaniel and Cajigas, Iahn and Jagid, Jonathan and Kanner, Andres and Elarjani, Turki and Bicchi, Manuel and Inati, Sara and Zaghloul, Kareem and Boerwinkle, Varina and Wyckoff, Sarah and Barot, Niravkumar and Gonzalez-Martinez, Jorge and Sarma, Sridevi},
doi = {10.18112/openneuro.ds003876.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003876.v1.0.2},
}
About This Dataset#
This dataset was updated and prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in [1] and [2].
This dataset comprises of de-identified subjects with interictal iEEG recordings possibly with sleep or awake state annotated. The subjects come from the following centers:
National Institute of Health (NIH)
Johns Hopkins Hospital (JHH)
University of Miami Florida Jackson Memorial Hospital (UMF)
Epilepsy Interictal Dataset
In the actual study, there is also data from Kansas University Medical Center (KUMC), University of Pittsburgh Medical Center and Cleveland Clinic, whose data is not shared due to restrictions imposed by the centers there.
Some subjects, namely with the
rnsprefix in their subject ID were treated with RNS rather then surgical resection/ablation.Derivatives
The processed data corresponding to the
source-sinkanalysis andhfocomparisons are shown in thederivatives/folder. The HFO analysis consists of two folders, one is an RMS detector and the other is a Hilbert detector. See the paper for details.Ties to Other Datasets
NIH
pt1, pt2, pt3, JHHjh103, jh105subjects are also datasets inhttps://openneuro.org/datasets/ds003029, where the ictal snapshots are stored. These correspond to the following: - pt1: pt01 - pt2: pt2 - pt3: pt3 - jh103: jh103 - jh105: jh105Moreover, the cclinic subjects are used in that study, but not open-access due to data sharing limitations at Cleveland Clinic. Those ictal datasets were analyzed in https://www.nature.com/articles/s41593-021-00901-w.
References
[1] Li, A., Huynh, C., Fitzgerald, Z. et al. Neural fragility as an EEG marker of the seizure onset zone. Nat Neurosci 24, 1465â1474 (2021). https://doi.org/10.1038/s41593-021-00901-w [2] Kristin M. Gunnarsdottir, Adam Li, Rachel J. Smith, Joon-Yi Kang, Nathan E. Crone, Anna Korzeniewska, Adam Rouse, Nathaniel Cameron, Iahn Cajigas, Sara Inati, Kareem A. Zaghloul, Varina L. Boerwinkle, Sarah Wyckoff, Nirav Barot, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Source-sink connectivity: a novel resting-state EEG marker of the epileptogenic zone. bioRxiv 2021.10.15.464594; doi: https://doi.org/10.1101/2021.10.15.464594 [3] Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 [4] Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D’Ambrosio, S., David, O., ⦠Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7
Cohort#
Dataset Statistics#
Age distribution by gender (n=36, range 16–62 yr, mean 35.8 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 5 h 45 min
Signal · Electrodes & live trace#
Live trace viewer — sub-rns011 · ses-extraoperative · task-interictal · run-01
Showing one representative recording out of
39 subjects and 54 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Epilepsy-iEEG-Interictal-Multicenter-Dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2021 |
Authors |
Gunnarsdottir, Kristin, Li, Adam, Smith, Rachel, Kang, Joon, Korzeniewska, Anna, Crone, Nathan, Rouse, Adam, Cheng, Jennifer, Kinsman, Michael, Landazuri, Patrick, Uysal, Utku, Ulloa, Carol, Cameron, Nathaniel, Cajigas, Iahn, Jagid, Jonathan, Kanner, Andres, Elarjani, Turki, Bicchi, Manuel, Inati, Sara, Zaghloul, Kareem, Boerwinkle, Varina, Wyckoff, Sarah, Barot, Niravkumar, Gonzalez-Martinez, Jorge, Sarma, Sridevi |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003876,
title = {Epilepsy-iEEG-Interictal-Multicenter-Dataset},
author = {Gunnarsdottir, Kristin and Li, Adam and Smith, Rachel and Kang, Joon and Korzeniewska, Anna and Crone, Nathan and Rouse, Adam and Cheng, Jennifer and Kinsman, Michael and Landazuri, Patrick and Uysal, Utku and Ulloa, Carol and Cameron, Nathaniel and Cajigas, Iahn and Jagid, Jonathan and Kanner, Andres and Elarjani, Turki and Bicchi, Manuel and Inati, Sara and Zaghloul, Kareem and Boerwinkle, Varina and Wyckoff, Sarah and Barot, Niravkumar and Gonzalez-Martinez, Jorge and Sarma, Sridevi},
doi = {10.18112/openneuro.ds003876.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds003876.v1.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003876 · Gunnarsdottir2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Epilepsy-iEEG-Interictal-Multicenter-Dataset
- Study:
ds003876(OpenNeuro)- Author (year):
Gunnarsdottir2021- Canonical:
—
Also importable as:
DS003876,Gunnarsdottir2021.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Epilepsy. Subjects: 39; 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
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/ds003876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003876 DOI: https://doi.org/10.18112/openneuro.ds003876.v1.0.2 NEMAR citation count: 3
Examples
>>> from eegdash.dataset import DS003876 >>> dataset = DS003876(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/ds003876").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 ds003876 to reproduce the tutorial on this dataset.
Citation
Gunnarsdottir, Kristin, Li, Adam, Smith, Rachel, Kang, Joon, Korzeniewska, Anna, … (2021). Epilepsy-iEEG-Interictal-Multicenter-Dataset. 10.18112/openneuro.ds003876.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003876.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset