Modelling and simulating different bands of EEG signals with the virtual brain

Al-Hossenat, Auhood and Wen, Paul and Li, Yan (2019) Modelling and simulating different bands of EEG signals with the virtual brain. International Journal of Electrical, Electronics and Data Communication, 7 (5). pp. 66-70. ISSN 2320-2084

Abstract

Electroencephalogram (EEG) have played an important role for studying the neurocognitive function such as delta and theta oscillations. In this study, we focused on modelling theta and delta two types of EEG signals by developing large-scale brain network model (LSBNM), consisting of eight brain regions areas (network nodes) in the left hemisphere. Each network node is simulated by a local neural model of Jansen and Rit (JR). The coupling of network nodes is constrained by the structural connectome for constructing the structural layout of the brain network model .The goal of this model was to simulate different rhythms of EEG ranging from delta to theta depending on the variations of parameters of JR model .The modelling and simulating are conducted with The Virtual Brain (TVB), a novel platform for modelling brain dynamics. Our result shew that the model is enable to simulate multi-bands of EEG: delta –range frequency of (2-3 Hz) and theta at frequency of 5Hz. This study can serve the clinical doctors and researchers to understand and study slow oscillation activity of EEG.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 21 Aug 2019 04:41
Last Modified: 21 Aug 2019 06:50
Uncontrolled Keywords: large-scale brain network modelling, neural masses model of JANSEN and Rit (JR-NMM), structural connectome, brain rhythms, The Virtual Brain Platform (TVB)
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
URI: http://eprints.usq.edu.au/id/eprint/36859

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