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iEEGinterictal iEEG during slow-wave sleep with HFO markings
BIDS Validation
ValidREADME
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] (https://github.com/adam2392/mne-hfo) [2].For more information, visit: https://github.com/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
Authors
- Fedele T
- Krayenbühl N
- Hilfiker P
- Adam Li
- Sarnthein J.
Available Modalities
iEEGVersions
Tasks
N/AUploaded by
Adam Li on 2021-02-01 - over 1 year agoLast Updated
2021-04-14 - about 1 year agoSessions
1Participants
20Dataset DOI
doi:10.18112/openneuro.ds003498.v1.0.1License
CC0Acknowledgements
Adam Li (github: adam2392) converted the dataset from the original format into BIDS. He used mne-bids to do so, and uses the dataset to validate mne-hfo, a Python open-source implementation of HFO detection algorithms. See 10.5281/zenodo.4485036.How to Acknowledge
N/AFunding
- N/A
References and Links
- N/A
Ethics Approvals
- N/A
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