eprintid: 8923
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/89/23
datestamp: 2023-11-09 16:20:51
lastmod: 2023-11-09 16:20:51
status_changed: 2023-11-09 16:13:51
type: conference_item
metadata_visibility: show
creators_name: Jatoi, M.A.
creators_name: Kamel, N.
creators_name: López, J.D.
creators_name: Faye, I.
creators_name: Malik, A.S.
title: MSP based source localization using EEG signals
ispublished: pub
keywords: Electroencephalography; Electrophysiology; Free energy; Neuroimaging; Neurons, Brain source localization; EEG source localization; Electromagnetic sources; Neuroimaging techniques; Source estimation; Source reconstruction; Sparse prior; Variational free energy, Inverse problems
note: cited By 4; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
abstract: The localization of brain sources due to which neural signals are generated is known as brain source localization. These signals are measured by various neuroimaging techniques such as MRI, EEG, PET and MEG. Nevertheless, when the neuroimaging technique is EEG, then it is specifically termed as EEG source localization. This problem is also referred to as EEG inverse problem. This problem is defined by forward problem and inverse problem. Because of ill-posed nature of EEG inverse problem, there exists uncertainty in the solution. This uncertainty in the solution can be reduced by imparting prior information within a Bayesian framework. Hence, Bayesian technique provides some assumptions related to prior information to quantify the solutions. This involves the information of cortical manifold to construct the set of possible regions where the neural activity occurs. This research work discusses and implements the source reconstruction for real time EEG dataset for Bayesian technique (multiple sparse priors (MSP)), classical LORETA and minimum norm techniques. The results are compared in terms of negative variational free energy, intensity level and computational complexity and it is shown that MSP has highest free energy and intensity level as compared to classical methods. © 2016 IEEE.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012025605&doi=10.1109%2fICIAS.2016.7824074&partnerID=40&md5=25e2076df2df83422fc4c6d6b293e131
id_number: 10.1109/ICIAS.2016.7824074
full_text_status: none
publication: International Conference on Intelligent and Advanced Systems, ICIAS 2016
refereed: TRUE
isbn: 9781509008452
citation:   Jatoi, M.A. and Kamel, N. and López, J.D. and Faye, I. and Malik, A.S.  (2017) MSP based source localization using EEG signals.  In: UNSPECIFIED.