DS003876#

Epilepsy-iEEG-Interictal-Multicenter-Dataset

Access recordings and metadata through EEGDash.

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

Modality: ieeg Subjects: 39 Recordings: 343 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

Epilepsy Interictal 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].

Summary

View full README

Epilepsy Interictal 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].

Summary

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)

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 rns prefix in their subject ID were treated with RNS rather then surgical resection/ablation.

Derivatives

The processed data corresponding to the source-sink analysis and hfo comparisons are shown in the derivatives/ 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, JHH jh103, jh105 subjects are also datasets in https://openneuro.org/datasets/ds003029, where the ictal snapshots are stored. These correspond to the following:

pt1: pt01
pt2: pt2
pt3: pt3
jh103: jh103
jh105: jh105

Moreover, 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., HÃ¶chenberger, 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., â¦ 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	`DS003876`
Title	Epilepsy-iEEG-Interictal-Multicenter-Dataset
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	doi:10.18112/openneuro.ds003876.v1.0.2
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: 39
Recordings: 343
Tasks: 3

Channels & sampling rate

Channels: 128 (20), 129 (16), 98 (8), 135 (8), 86 (8), 111 (4), 110 (4), 47 (4), 101 (4), 107 (2), 121 (2), 95 (2), 146 (2), 168 (2), 170 (2), 114 (2), 118 (2), 182 (2), 147 (2), 190 (2), 193 (2), 134 (2), 125 (2), 186 (2), 46 (2)
Sampling rate (Hz): 1000.0 (50), 2000.0 (14), 999.4121105232217 (12), 1024.0 (10), 999.9999999999999 (8), 1024.5997950800408 (4), 499.7071044492829 (4), 500.0 (4), 512.0 (2)
Duration (hours): 0.0

API Reference#

Use the DS003876 class to access this dataset programmatically.

class eegdash.dataset.DS003876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003876. 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

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/ds003876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003876

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, 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