DS003498: ieeg dataset, 20 subjects#
interictal iEEG during slow-wave sleep with HFO markings
Citation: Fedele T, Krayenbühl N, Hilfiker P, Adam Li, Sarnthein J. (2019). interictal iEEG during slow-wave sleep with HFO markings. 10.18112/openneuro.ds003498.v1.0.1
20-participant iEEG dataset — interictal iEEG during slow-wave sleep with HFO markings.
Quickstart#
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)
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'])
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#
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.
Zurich iEEG HFO Dataset
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
View full README
Zurich iEEG HFO Dataset
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.tsvfile per dataset run. The format is similar tomne-hfoand can be easily read in usingmne-bidsand/ormne-python.Each row in the events.tsv file corresponds to a HFO detected in the original source dataset. The
trial_typecolumn stores the information pertaining type of HFO (e.g.ripple,frfor fast ripple, orfrandrfor 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
Cohort#
Dataset Statistics#
Age distribution by gender (n=19, range 17–52 yr, mean 32.5 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 2000.0 Hz (n=385 recordings)
Total recording duration: 32 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-interictalsleep · run-14
Showing one representative recording out of
20 subjects and 385 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 |
interictal iEEG during slow-wave sleep with HFO markings |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Fedele T, Krayenbühl N, Hilfiker P, Adam Li, Sarnthein J. |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003498 · Fedele2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003498(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
interictal iEEG during slow-wave sleep with HFO markings
- Study:
ds003498(OpenNeuro)- Author (year):
Fedele2021- Canonical:
—
Also importable as:
DS003498,Fedele2021.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
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/ds003498 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003498 DOI: https://doi.org/10.18112/openneuro.ds003498.v1.0.1 NEMAR citation count: 3
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: 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/ds003498").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 ds003498 to reproduce the tutorial on this dataset.
Citation
Fedele T, Krayenbühl N, Hilfiker P, Adam Li, Sarnthein J. (2019). interictal iEEG during slow-wave sleep with HFO markings. 10.18112/openneuro.ds003498.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003498.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset