DS004100#

HUP iEEG Epilepsy Dataset

Access recordings and metadata through EEGDash.

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 (2022). HUP iEEG Epilepsy Dataset. 10.18112/openneuro.ds004100.v1.1.3

Modality: ieeg Subjects: 57 Recordings: 1341 License: CC0 Source: openneuro Citations: 21.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

<h1>HUP iEEG dataset</h1>

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

Dataset Information#

Dataset ID	`DS004100`
Title	HUP iEEG Epilepsy Dataset
Year	2022
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	doi:10.18112/openneuro.ds004100.v1.1.3
Source links	OpenNeuro \| NeMAR \| Source URL

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: 57
Recordings: 1341
Tasks: 2

Channels & sampling rate

Channels: 122 (42), 128 (36), 118 (34), 172 (30), 126 (28), 104 (26), 180 (24), 127 (24), 82 (24), 96 (24), 109 (14), 136 (14), 121 (14), 74 (14), 108 (14), 190 (14), 80 (14), 92 (14), 120 (14), 174 (14), 149 (14), 102 (14), 117 (14), 163 (12), 98 (12), 162 (10), 186 (10), 63 (10), 71 (10), 52 (10), 116 (10), 59 (10), 88 (10), 164 (10), 100 (10), 90 (8), 105 (8), 61 (8), 85 (6), 192 (4), 94 (4), 232 (2)
Sampling rate (Hz): 512.0 (330), 1024.0 (156), 500.0 (138), 256.0 (14)
Duration (hours): 0.0

API Reference#

Use the DS004100 class to access this dataset programmatically.

class eegdash.dataset.DS004100(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004100. 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

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/ds004100 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004100

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, 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