An Experimental Sensitivity Analysis of Gaussian and Non-Gaussian Based Methods for Dynamic Modeling in EEG Signal Processing

Background

Sensitivity analysis is generally defined as the study of how the outputs of a method change under a given change of its inputs. It has many applications, such as testing the stability of a method or testing its robustness when the main assumptions no longer hold true. Sensitivity analysis has been used in many different fields, such as risk assessment, algorithm characterization, and medical studies (Saltelli, et al., 2008). Within medical research, there are many applications of sensitivity analysis on EEG signals, methods and equipment, such as: testing the influence of different factors (the scalp, electrodes…) on the EEG (Rice, Rorden, Little, & Parra, 2013); exploring the behavior of head conductivity and EEG models (Vallaghe, 2009; Dannhauer, Lanfer, Wolters, & Knösche, 2011); or performing meta-analysis studies (Gao & Raine, 2009).