Research Article |
System Modelling and Identification for EEG Monitoring using Random Vector Functional Link Network
Author(s): Rakesh Kumar Pattanaik1, Binod Kumar Pattanayak2 and Mihir Narayan Mohanty3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 11, Issue 1
Publisher : FOREX Publication
Published : 30 January 2023
e-ISSN : 2347-470X
Page(s) : 10-14
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%.
Keywords: System Modelling
, Identification
, EEG
, ELM
, Random Vector functional link network
.
Rakesh Kumar Pattanaik*, Institute of Technical Education & Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India; Email: rakeshpattanaik.888@gmail.com
Binod Kumar Pattanayak, Institute of Technical Education & Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India; Email: binodpattanayak@soa.ac.in
Mihir Narayan Mohanty, Institute of Technical Education & Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India; Email: mihir.n.mohanty@gmail.com
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