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