HFOs detectors are still an open field of discussion, I think you won't find a unique verified method. From my experience, the methods that use a first stage to select events (for example with a hilbert transform and a threshold) and then use time frequency methods to eliminate false positive are the best ones. Maybe you can take a look at the work done by people from Marseille: Article What are the assets and weaknesses of HFO detectors? A bench...

Hi Fernando. You might not need a toolbox. Extracting HFO from a raw M/EEG/LFP signal can be done via bandpass filtering + Hilbert transform and extracting magnitude. Detecting events could be based on some data-driven threshold, e.g., >3std or perhaps the top 5% of peaks. Sometimes, writing your own code is better (e.g., more customizable) than a toolbox that might be designed for some other purpose.

If you would like to learn more about neural signal process, time series analysis, and time-frequency dynamics, I have a lot of resources like books and lectures and online courses linked on my website: mikexcohen.com

Mike

I know that I can do it manually, but probably is better a verified method already published.

HFOs detectors are still an open field of discussion, I think you won't find a unique verified method. From my experience, the methods that use a first stage to select events (for example with a hilbert transform and a threshold) and then use time frequency methods to eliminate false positive are the best ones. Maybe you can take a look at the work done by people from Marseille: Article What are the assets and weaknesses of HFO detectors? A bench...