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

Authors: Rakesh Kumar Pattanaik; Rinky Dwivedi; Mihir Narayan Mohanty

Addresses: Department of Electronics and Communication Engineering, ITER (FET), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India ' Department of Computer Science Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India ' Department of Electronics and Communication Engineering, ITER (FET), Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

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.

Keywords: variational mode decomposition; linear time-invariant; random vector functional link network; RVFLN; nonlinear system identification; electroencephalogram; EEG.

DOI: 10.1504/IJNVO.2022.127601

International Journal of Networking and Virtual Organisations, 2022 Vol.27 No.2, pp.125 - 142

Received: 26 Feb 2022
Received in revised form: 24 May 2022
Accepted: 04 Jul 2022
Published online: 12 Dec 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article