Methodology Design of the Correlation Between EEG Signals and Brain Regions Mapping in Panic Attacks

Abstract

While EEG signals provide insight into brain activity, computational methods from software engineering and artificial intelligence can contribute to the development of a wide range of solutions. This research explores the difficulty of monitoring brain activity at the time of a panic attack on a common basis, provided the lack of methodologies to identify correlating factors in brain activity before and after a panic attack to reference the event and provide the healthcare specialist with data-driven tools based on the brain activity. The methodology presented is a transversal proposal of Lean UX as a bridge for the health specialist involvement per the designed stages of software solutions based on a case study to monitor brain activity at the time of a panic attack, leading to common ground solutions to identify its triggers. Additionally, control variables were identified to improve the data quality, and a visualization tool was used to display the results and obtain information on the types of users while improving the UX and UI.