System Modelling and Identification for EEG Monitoring using Random Vector Functional Link Network

Abstract

Brain signal research occupies a special position in recent biomedical research in recent times. In this work, the authors try to develop a model for monitoring the EEG signal of the patient. It is the extrinsic application of the system identification problem. The Random Vector functional link network (RVFLN) model as the variant of Neural Network, is proposed for the dynamic modeling of a practical system. RVFLN is a fast-learning feed-forward network and does not need iterative tuning that reduces the model's computational complexity and faster training performance. The model is verified with Electroencephalogram (EEG) signal for identification so that it is well suitable for tracking and monitoring systems for patients. The performance of RVFLN is compared with existing models. From the result analysis, it is found that the performance of the proposed RVFLN is most impressive with an efficiency of 99.86%.