Modeling the human visual system using the white-noise approach

Engineering analysis has been utilized with great success over the past few decades to characterize physiological systems. For example, system identification approaches have been developed to describe the linear and nonlinear properties of such systems in a very general way, allowing for new insights to be made into physiological function. Recent work has seen the application of these techniques to the analysis of the human visual system using the electroencephalogram (EEG). The resulting linear impulse response estimate of visual function is known as the VESPA. This paper employs a nonlinear extension of the VESPA method to quantify the relative contribution of linear and quadratic processes to the EEG in response to novel visual stimuli.