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EEG-based brain source localization using visual stimuli

Jatoi, M.A. and Kamel, N. and Malik, A.S. and Faye, I. and Bornot, J.M. and Begum, T. (2016) EEG-based brain source localization using visual stimuli. International Journal of Imaging Systems and Technology, 26 (1). pp. 55-64.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low-resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others. © 2016 Wiley Periodicals, Inc.

Item Type:Article
Impact Factor:cited By 10
Uncontrolled Keywords:Bayesian networks; Electroencephalography; Electrophysiology; Finite difference method; Inverse problems; Magnetic resonance imaging; Signal processing, Brain activation; Brain source localization; Finitedifference methods (FDM); High-resolution methods; LORETA; Low resolution brain electromagnetic tomographies; sLORETA; Source localization, Brain
ID Code:25583
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 09:59
Last Modified:27 Aug 2021 09:59

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