DS004100: ieeg dataset, 57 subjects#
HUP iEEG Epilepsy Dataset
Citation: John M. Bernabei, Adam Li, Andrew Y. Revell, Rachel J. Smith, Kristin M. Gunnarsdottir, Ian Z. Ong, Kathryn A. Davis, Nishant Sinha, Sridevi Sarma, Brian Litt (2019). HUP iEEG Epilepsy Dataset. 10.18112/openneuro.ds004100.v1.1.3
57-participant iEEG dataset — HUP iEEG Epilepsy Dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004100
dataset = DS004100(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004100(cache_dir="./data", subject="01")
Advanced query
dataset = DS004100(
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{ds004100,
title = {HUP iEEG Epilepsy Dataset},
author = {John M. Bernabei and Adam Li and Andrew Y. Revell and Rachel J. Smith and Kristin M. Gunnarsdottir and Ian Z. Ong and Kathryn A. Davis and Nishant Sinha and Sridevi Sarma and Brian Litt},
doi = {10.18112/openneuro.ds004100.v1.1.3},
url = {https://doi.org/10.18112/openneuro.ds004100.v1.1.3},
}
About This Dataset#
<h3>HUP iEEG dataset</h3>
This dataset was prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in Kini & Bernabei et al., Brain (2019) [1], and Bernabei & Sinha et al., Brain (2022) [2]. <h3>Dataset description</h3> These files contain de-identified patient data collected as part of surgical treatment for drug resistant epilepsy at the Hospital of the University of Pennsylvania. Each of the 58 subjects underwent intracranial EEG with subdural grid, strip, and depth electrodes (ECoG) or purely stereotactically-placed depth electrodes (SEEG). Each patient also underwent subsequent treatment with surgical resection or laser ablation. Electrophysiologic data for both interictal and ictal periods is available, as are electrode localizations in ICBM152 MNI space. Furthermore, clinically-determined seizure onset channels are provided, as are channels which overlap with the resection/ablation zone, which was rigorously determined by segmenting the resection cavity. <h3>BIDS Conversion</h3> MNE-BIDS was used to convert the dataset into BIDS format. <h3>References</h3> [1] Kini L.*, Bernabei J.M.*, Mikhail F., Hadar P., Shah P., Khambhati A., Oechsel K., Archer R., Boccanfuso J.A., Conrad E., Stein J., Das S., Kheder A., Lucas T.H., Davis K.A., Bassett D.S., Litt B., Virtual resection predicts surgical outcome for drug resistant epilepsy. Brain, 2019. [2] Bernabei J.M.*, Sinha N.*, Arnold T.C., Conrad E., Ong I., Pattnaik A.R., Stein J.M., Shinohara R.T., Lucas T.H., Bassett D.S., Davis K.A., Litt B., Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain, 2022 [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=56, range 16–59 yr, mean 34.3 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 25 h
Signal · Electrodes & live trace#
Live trace viewer — sub-HUP064 · ses-presurgery · task-interictal · run-02
Showing one representative recording out of
57 subjects and 319 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 · 106 sensors — 106 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 |
HUP iEEG Epilepsy Dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
John M. Bernabei, Adam Li, Andrew Y. Revell, Rachel J. Smith, Kristin M. Gunnarsdottir, Ian Z. Ong, Kathryn A. Davis, Nishant Sinha, Sridevi Sarma, Brian Litt |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004100,
title = {HUP iEEG Epilepsy Dataset},
author = {John M. Bernabei and Adam Li and Andrew Y. Revell and Rachel J. Smith and Kristin M. Gunnarsdottir and Ian Z. Ong and Kathryn A. Davis and Nishant Sinha and Sridevi Sarma and Brian Litt},
doi = {10.18112/openneuro.ds004100.v1.1.3},
url = {https://doi.org/10.18112/openneuro.ds004100.v1.1.3},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004100 · Bernabei2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004100(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
HUP iEEG Epilepsy Dataset
- Study:
ds004100(OpenNeuro)- Author (year):
Bernabei2022- Canonical:
—
Also importable as:
DS004100,Bernabei2022.Modality:
ieeg; Experiment type:Clinical/Intervention; Subject type:Epilepsy. Subjects: 57; recordings: 319; tasks: 2.- 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/ds004100 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004100 DOI: https://doi.org/10.18112/openneuro.ds004100.v1.1.3 NEMAR citation count: 21
Examples
>>> from eegdash.dataset import DS004100 >>> dataset = DS004100(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/ds004100").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 ds004100 to reproduce the tutorial on this dataset.
Citation
John M. Bernabei, Adam Li, Andrew Y. Revell, Rachel J. Smith, Kristin M. Gunnarsdottir, … (2019). HUP iEEG Epilepsy Dataset. 10.18112/openneuro.ds004100.v1.1.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004100.v1.1.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset