DS003498#

interictal iEEG during slow-wave sleep with HFO markings

Access recordings and metadata through EEGDash.

Citation: Fedele T, Krayenbühl N, Hilfiker P, Adam Li, Sarnthein J. (2021). interictal iEEG during slow-wave sleep with HFO markings. 10.18112/openneuro.ds003498.v1.0.1

Modality: ieeg Subjects: 20 Recordings: 385 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003498

dataset = DS003498(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 = DS003498(cache_dir="./data", subject="01")

Advanced query

dataset = DS003498(
    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{ds003498,
  title = {interictal iEEG during slow-wave sleep with HFO markings},
  author = {Fedele T and Krayenbühl N and Hilfiker P and Adam Li and Sarnthein J.},
  doi = {10.18112/openneuro.ds003498.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003498.v1.0.1},
}

About This Dataset#

Zurich iEEG HFO Dataset

This dataset was obtained from the publication [1]. There are 20 subjects with HFO events. We converted the dataset into BIDS format. The original uploader: adam2392 obtained explicit permission from the authors of the dataset to upload this to openneuro. Adam worked on an open-source Python implementation of HFO detection algorithms, and uses this dataset in validation. Even though the publication involves a Morphology HFO detector, we have implemented our interpretation of the RMS, LineLength and Hilbert detectors in the [mne-hfo repository] (adam2392/mne-hfo) [2].For more information, visit: adam2392/mne-hfo.

Note from the paper

“We excluded all electrode contacts where electrical stimulation evoked motor or language responses (Table S1).

View full README

Zurich iEEG HFO Dataset

This dataset was obtained from the publication [1]. There are 20 subjects with HFO events. We converted the dataset into BIDS format. The original uploader: adam2392 obtained explicit permission from the authors of the dataset to upload this to openneuro. Adam worked on an open-source Python implementation of HFO detection algorithms, and uses this dataset in validation. Even though the publication involves a Morphology HFO detector, we have implemented our interpretation of the RMS, LineLength and Hilbert detectors in the [mne-hfo repository] (adam2392/mne-hfo) [2].For more information, visit: adam2392/mne-hfo.

Note from the paper

“We excluded all electrode contacts where electrical stimulation evoked motor or language responses (Table S1). In TLE patients, we included only the 3 most mesial bipolar channels”.

BIDS Conversion

MNE-BIDS was used to convert the dataset into BIDS format. The code inside code/ was used to generate the data.

HFO Events From Original Paper

The HFO events from the original paper that were validated and detected are stored in the *events.tsv file per dataset run. The format is similar to mne-hfo and can be easily read in using mne-bids and/or mne-python. Each row in the events.tsv file corresponds to a HFO detected in the original source dataset. The trial_type column stores the information pertaining type of HFO (e.g. ripple, fr for fast ripple, or frandr for fast ripple and ripple). The channel name (possibly in bipolar reference) is "-" character delimited and appended to the type of HFO with a "_" separating. For example: <hfo_type>_<channel_name> is the form.

Reference Dataset

The following website was where the original data was downloaded. http://crcns.org/data-sets/methods/ieeg-1

References

[1] Fedele T, Burnos S, Boran E, Krayenbühl N, Hilfiker P, Grunwald T, Sarnthein J. Resection of high frequency oscillations predicts seizure outcome in the individual patient. Scientific Reports. 2017;7(1):13836. https://www.nature.com/articles/s41598-017-13064-1 doi:10.1038/s41598-017-13064-1 [2] Dataset meta analysis with mne-hfo. 10.5281/zenodo.4485036 [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	`DS003498`
Title	interictal iEEG during slow-wave sleep with HFO markings
Year	2021
Authors	Fedele T, Krayenbühl N, Hilfiker P, Adam Li, Sarnthein J.
License	CC0
Citation / DOI	10.18112/openneuro.ds003498.v1.0.1
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: 20
Recordings: 385
Tasks: —

Channels & sampling rate

Channels: 64 (292), 40 (146), 42 (86), 74 (70), 16 (58), 50 (56), 52 (26), 48 (26), 30 (10)
Sampling rate (Hz): 2000.0
Duration (hours): 0.0

API Reference#

Use the DS003498 class to access this dataset programmatically.

class eegdash.dataset.DS003498(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003498. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 20; recordings: 385; tasks: 0.

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/ds003498 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003498

Examples

>>> from eegdash.dataset import DS003498
>>> dataset = DS003498(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