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