Correntropy coefficient analysis of fMRI using reference model

A non-invasive functional MRI (fMRI) has emerged effective in the investigation of the functionality of human brain. However, detection of functional activation is often complicated by the presence of noise. In this paper, we propose an approach based on correntropy, a recently introduced measure wh...

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Bibliographic Details
Main Authors: Maheshwari, Harish Kumar., Siyal, Muhammad Yakoob.
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/84645
http://hdl.handle.net/10220/11799
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author Maheshwari, Harish Kumar.
Siyal, Muhammad Yakoob.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Maheshwari, Harish Kumar.
Siyal, Muhammad Yakoob.
author_sort Maheshwari, Harish Kumar.
collection NTU
description A non-invasive functional MRI (fMRI) has emerged effective in the investigation of the functionality of human brain. However, detection of functional activation is often complicated by the presence of noise. In this paper, we propose an approach based on correntropy, a recently introduced measure which incorporates both amplitude and temporal structure characteristics of time series in single functional measure. Using correntropy coefficient as the test statistic, nonparametric approach is carried out via pre-whitening resampling transform to calculate the statistical p-values. Experimental results suggest that proposed method enables more effective brain activation detection compared with mutual information.
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spelling ntu-10356/846452020-03-07T13:24:44Z Correntropy coefficient analysis of fMRI using reference model Maheshwari, Harish Kumar. Siyal, Muhammad Yakoob. School of Electrical and Electronic Engineering International Conference on Control Automation Robotics & Vision (12th : 2012 : Guangzhou, China) DRNTU::Engineering::Electrical and electronic engineering A non-invasive functional MRI (fMRI) has emerged effective in the investigation of the functionality of human brain. However, detection of functional activation is often complicated by the presence of noise. In this paper, we propose an approach based on correntropy, a recently introduced measure which incorporates both amplitude and temporal structure characteristics of time series in single functional measure. Using correntropy coefficient as the test statistic, nonparametric approach is carried out via pre-whitening resampling transform to calculate the statistical p-values. Experimental results suggest that proposed method enables more effective brain activation detection compared with mutual information. 2013-07-18T01:08:29Z 2019-12-06T15:48:53Z 2013-07-18T01:08:29Z 2019-12-06T15:48:53Z 2012 2012 Conference Paper Maheshwari, H. K., & Siyal, M. Y. (2012). Correntropy coefficient analysis of fMRI using reference model. 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), 396-400. https://hdl.handle.net/10356/84645 http://hdl.handle.net/10220/11799 10.1109/ICARCV.2012.6485191 en © 2012 IEEE.
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Maheshwari, Harish Kumar.
Siyal, Muhammad Yakoob.
Correntropy coefficient analysis of fMRI using reference model
title Correntropy coefficient analysis of fMRI using reference model
title_full Correntropy coefficient analysis of fMRI using reference model
title_fullStr Correntropy coefficient analysis of fMRI using reference model
title_full_unstemmed Correntropy coefficient analysis of fMRI using reference model
title_short Correntropy coefficient analysis of fMRI using reference model
title_sort correntropy coefficient analysis of fmri using reference model
topic DRNTU::Engineering::Electrical and electronic engineering
url https://hdl.handle.net/10356/84645
http://hdl.handle.net/10220/11799
work_keys_str_mv AT maheshwariharishkumar correntropycoefficientanalysisoffmriusingreferencemodel
AT siyalmuhammadyakoob correntropycoefficientanalysisoffmriusingreferencemodel