Abstract
From the viewpoint of stochastic inverse problem, identification of transfer functions in feedback systems has been examined by the previous papers[1][2]. In this paper it is applied for a new approach to electroencephalography (EEG). Some transfer functions between measurement regions of EEG are identified by our method of inverse problem in a stochastic feedback system. In our method for stochastic inverse problem an innovation model must be self-consistent: Since an innovation model equivalent to correlation functions has minimum phase and suitable properties from the theoretical considerations[3], innovation models with their properties are selected for dynamic analysis of EEG. Hence, a stochastic feedback model in the stationary process can be determined from EEG time series data of measurement regions, and we can obtain transfer functions between measurement regions.
