Application of nonlinear system identification for EEG modelling using VMD-based deep random vector functional link network

Abstract

In this paper, the EEG signal is considered for the development of the model. As the signal is nonlinear and non-stationary, the model is designed accordingly which is similar to nonlinear dynamic system identification. Initially, the signal is decomposed by a robust variational mode decomposition method for which the basic noise components are eliminated. Later, a kurtosis index method is applied to choose the best band-limited intrinsic mode functions (BLIMFs) based on their clean coefficient the model is developed using a random vector functional link neural network (RVFLN) for identification. The use of deep RVFLN provides better results as compared to simple RVFLN as explained in the result section. For verification of the system's robustness, three different epileptic signals known as pre-ictal, inter-ictal and ictal are experienced in this piece of work.