Figure 1. Generic algorithm flow chart of automated HFOs detection.

A) Schematic of automatic HFOs detection. The brain activity is recorded with a wide bandwidth acquisition system. Then, the raw broadband electrophysiologic recording is “whitened” to better highlight the low amplitude HFOs transients. A wide range of features can be extracted to aid classification. After the feature extraction through different methods, the candidate events can be classified using a wide range of classification approaches, like machine learning. The output of these detectors should be evaluated and have comparable outputs from other detectors and laboratories. Red boxes correspond to the advances in HFOs detection and blue boxes are the established flow of HFOs detection. B) Three HFOs detected in a wide bandwidth recording from human brain. Each figure is composed, from top to bottom, of the raw signal (in blue) with the automatically detected HFOs (red) in the signal, and the continuous time frequency image.