eprintid: 10154
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/01/54
datestamp: 2023-11-09 16:36:47
lastmod: 2023-11-09 16:36:47
status_changed: 2023-11-09 16:30:43
type: conference_item
metadata_visibility: show
creators_name: Jatoi, M.A.
creators_name: Kamel, N.
creators_name: Gaho, A.A.
creators_name: Dharejo, F.A.
title: Bayesian Framework based Brain Source Localization Using High SNR EEG Data
ispublished: pub
keywords: Bioinformatics; Biophysics; Boundary element method; Brain; Electrophysiology; Finite volume method; Free energy; Gaussian noise (electronic); Inverse problems; Neuroimaging; Sailing vessels; Signal to noise ratio, Bayesian frameworks; Boundary-element methods; Brain source localization; Forward modeling; Further development; Health care application; High SNR; Localization errors; Multiple sparse prior; Sparse prior, Electroencephalography
note: cited By 0; Conference of 5th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2018 ; Conference Date: 22 November 2018 Through 23 November 2018; Conference Code:144743
abstract: The multipurpose application of brain source localization in the domain of biomedical engineering has aggrandized the ways for its further development for various healthcare applications. Various brain regions are activated due to different mental and physical tasks. These sources can be localized using different optimization algorithms. This localization information is usable for diagnoses of brain disorders such as epilepsy, Schizophrenia, depression and Alzheimer. The brain signals are recorded through neuroimaging techniques such as MEG, EEG, fMRI and PET etc. Nevertheless, when EEG signals are used to reconstruct the active brain sources, then its termed as EEG source localization. The localization involves two phases: forward modeling and inverse modeling. The forward modeling is carried out to model the head using various numerical techniques. Some of them are finite element method (FEM), boundary element method (BEM) and finite volume method (FVM). Furthermore, the solution of inverse problem is realized through usage of various optimization techniques such as minimum norm estimation (MNE), low resolution brain electromagnetic tomography (LORETA), multiple signal classification (MUSIC) and multiple sparse priors (MSP). This research work discusses the results based on synthetically generated EEG data at an SNR level of 12 dB with Gaussian noise added linearly in data matrix. For forward modelling, boundary element method with 1 μA dipole amplitude at dipole frequency of 20Hz is utilized. The dipoles' location is set randomly at 2000 and 5700 at CTF. The techniques mentioned above are applied on data and are compared with latest multiple sparse priors (MSP). The evaluation of results is done based on comparative analysis of free energy and localization error. The results shows superiority of MSP over classical algorithms. © 2018 IEEE.
date: 2018
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062883677&doi=10.1109%2fICETAS.2018.8629157&partnerID=40&md5=b4818e7b1ea9f5b92d4a22798b5db893
id_number: 10.1109/ICETAS.2018.8629157
full_text_status: none
publication: 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences, ICETAS 2018
refereed: TRUE
isbn: 9781538679661
citation:   Jatoi, M.A. and Kamel, N. and Gaho, A.A. and Dharejo, F.A.  (2018) Bayesian Framework based Brain Source Localization Using High SNR EEG Data.  In: UNSPECIFIED.