Correlated EEG Signals Simulation Based on Artificial Neural Networks

Author:

Tomasevic Nikola M.1,Neskovic Aleksandar M.2,Neskovic Natasa J.2

Affiliation:

1. University of Belgrade, The Mihajlo Pupin Institute, Volgina 15, 11060 Belgrade, Serbia

2. School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, P. O. Box 3554, 11120 Belgrade, Serbia

Abstract

In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

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