Document Detail


Characterization of event related potentials using information theoretic distance measures.
MedLine Citation:
PMID:  15132499     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Analysis of event-related potentials (ERPs) using signal processing tools has become extremely widespread in recent years. Nonstationary signal processing tools such as wavelets and time-frequency distributions have proven to be especially effective in characterizing the transient phenomena encountered in event-related potentials. In this paper, we focus on the analysis of event-related potentials collected during a psychological experiment where two groups of subjects, spider phobics and snake phobics, are shown the same set of stimulus: A blank stimulus, a neutral stimulus and a spider stimulus. We introduce a new approach, based on time-frequency distributions, for analyzing the ERPs. The difference in brain activity before and after a stimulus is presented is quantified using distance measures as adapted to the time-frequency plane. Three different distance measures, including a new information theoretic distance measure, are applied on the time-frequency plane to discriminate between the responses of the two groups of subjects. The results illustrate the effectiveness of using distance measures combined with time-frequency distributions in differentiating between the two classes of subjects and the different regions of the brain.
Authors:
Selin Aviyente; Linda A W Brakel; Ramesh K Kushwaha; Michael Snodgrass; Howard Shevrin; William J Williams
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Publication Detail:
Type:  Clinical Trial; Controlled Clinical Trial; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  51     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2004 May 
Date Detail:
Created Date:  2004-05-10     Completed Date:  2004-06-10     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  737-43     Citation Subset:  IM    
Affiliation:
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. aviyente@egr.msu.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Brain / physiopathology*
Electroencephalography / methods*
Evoked Potentials*
Information Theory
Models, Neurological
Models, Statistical*
Phobic Disorders / diagnosis*,  physiopathology*,  psychology
Signal Processing, Computer-Assisted*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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