Spontaneous High Frequency Oscillations (HFOs) have been considered as emerging specific biomarkers of the epileptogenic region. As a first issue, a significant difference in the implementation of automatic HFOs detection methods can sometimes occur between researchers. In addition, clinicians are not even particularly familiar with the concept of signal and image processing, and programming skills. To overcome these limitations, we propose a plug-and-play interactive Graphical User Interface (GUI) that incorporates an amalgamation of six validated methods used for detecting and quantifying of HFOs events. As a second issue, the most automated HFOs detection methods to date have a high false detection rate and low specificity, ranging, in some cases up to 80% and below 37% respectively. Therefore, the eventual utilization of HFOs detection algorithms in clinical settings requires a checking step to save clinically relevant HFOs and remove spurious oscillations from the detection results. As a last issue addressed in the present study, the major previous HFOs studies have been limited only to the detection and classification of HFOs, but only a few studies have been conducted to efficiently follow the neural dynamics of epileptic focus by studying HFOs characteristics through different brain regions and clinical stages. Therefore, in our software, the brain mapping of HFOs characteristics is done based on the duration, the inter-duration, the average frequency, and the power of HFOs. The present developed software may be considered helpful for understanding the functional significance of HFOs and also to reduce the interaction gap between fundamental research and applied clinical practice related to HFOs. © 2023 Elsevier Ltd