Objective selection of EEG late potentials through residual dependence estimation of independent components
Objective selection of EEG late potentials through residual dependence estimation of independent components
 
  This paper presents a novel method to objectively select electroencephalographic (EEG) cortical sources estimated by independent component analysis (ICA) in event-related potential (ERP) studies. 
A proximity measure based on mutual information is employed to estimate residual dependences of the components that are then hierarchically clustered based on these residual dependences.
Next, the properties of each group of components are evaluated
at each level of the hierarchical tree by two indices that aim to assess both cluster tightness and physiological reliability through a template matching process.
These two indices are combined in three different approaches to bringto light the hierarchical structure of the cluster organizations. 
Our method is tested on a set of experiments with the purpose of enhancing late positive ERPs elicited by emotional picture stimuli. 
Results suggest that the best way to look for physiologically plausible late positive potential (LPP) sources is to explore in depth the tightness of those clusters that, taken together, best
resemble the template. 
According to our results, after brain sources clustering,
LPPs are always identified more accurately than from ensemble-averaged raw data. 
Since the late components of an ERP involve the same associative areas, regardless of the modality of stimulation or specific tasks administered, the proposed method can be simply adapted to other ERP studies, and extended
from psychophysiological
  independent component analysis, clustering, EEG event-related
potentials
  
  
  779-794
  
    
      Milanesi, M.
      
        df81551a-ce56-4d94-8850-65416b92bb44
      
     
  
    
      James, C. J.
      
        79a03a14-ca4c-4247-8792-7cd771554797
      
     
  
    
      Martini, N.
      
        dfe71306-8742-4229-8d34-bcfeb1c0e650
      
     
  
    
      Menicucci, D.
      
        1d594e51-0536-457b-a1cd-7a7c1767f1f1
      
     
  
    
      Gemignani, A.
      
        bc8879d9-2063-4b78-a629-80f1a2ea9cb4
      
     
  
    
      Ghelarducci, B.
      
        36ab1580-0afe-455e-b898-e1d04971168a
      
     
  
    
      Landini, L.
      
        19d472c7-d12d-4674-87f9-6e57c563b553
      
     
  
  
   
  
  
    
      August 2009
    
    
  
  
    
      Milanesi, M.
      
        df81551a-ce56-4d94-8850-65416b92bb44
      
     
  
    
      James, C. J.
      
        79a03a14-ca4c-4247-8792-7cd771554797
      
     
  
    
      Martini, N.
      
        dfe71306-8742-4229-8d34-bcfeb1c0e650
      
     
  
    
      Menicucci, D.
      
        1d594e51-0536-457b-a1cd-7a7c1767f1f1
      
     
  
    
      Gemignani, A.
      
        bc8879d9-2063-4b78-a629-80f1a2ea9cb4
      
     
  
    
      Ghelarducci, B.
      
        36ab1580-0afe-455e-b898-e1d04971168a
      
     
  
    
      Landini, L.
      
        19d472c7-d12d-4674-87f9-6e57c563b553
      
     
  
       
    
 
  
    
      
  
  
  
  
  
  
    Milanesi, M., James, C. J., Martini, N., Menicucci, D., Gemignani, A., Ghelarducci, B. and Landini, L.
  
  
  
  
   
    (2009)
  
  
    
    Objective selection of EEG late potentials through residual dependence estimation of independent components.
  
  
  
  
    Physiological Measurement, 30 (8), .
  
   (doi:10.1088/0967-3334/30/8/004). 
  
  
   
  
  
  
  
  
   
  
    
      
        
          Abstract
          This paper presents a novel method to objectively select electroencephalographic (EEG) cortical sources estimated by independent component analysis (ICA) in event-related potential (ERP) studies. 
A proximity measure based on mutual information is employed to estimate residual dependences of the components that are then hierarchically clustered based on these residual dependences.
Next, the properties of each group of components are evaluated
at each level of the hierarchical tree by two indices that aim to assess both cluster tightness and physiological reliability through a template matching process.
These two indices are combined in three different approaches to bringto light the hierarchical structure of the cluster organizations. 
Our method is tested on a set of experiments with the purpose of enhancing late positive ERPs elicited by emotional picture stimuli. 
Results suggest that the best way to look for physiologically plausible late positive potential (LPP) sources is to explore in depth the tightness of those clusters that, taken together, best
resemble the template. 
According to our results, after brain sources clustering,
LPPs are always identified more accurately than from ensemble-averaged raw data. 
Since the late components of an ERP involve the same associative areas, regardless of the modality of stimulation or specific tasks administered, the proposed method can be simply adapted to other ERP studies, and extended
from psychophysiological
        
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  More information
  
    
      Published date: August 2009
 
    
  
  
    
  
    
  
    
  
    
  
    
     
    
  
    
     
        Keywords:
        independent component analysis, clustering, EEG event-related
potentials
      
    
  
    
  
    
  
  
  
    
  
  
        Identifiers
        Local EPrints ID: 79181
        URI: http://eprints.soton.ac.uk/id/eprint/79181
        
          
        
        
        
          ISSN: 0967-3334
        
        
          PURE UUID: 4f4709b1-4705-448f-8757-0bfe8f35ab3c
        
  
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
  
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  Date deposited: 15 Mar 2010
  Last modified: 14 Mar 2024 00:28
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      Contributors
      
          
          Author:
          
            
            
              M. Milanesi
            
          
        
      
          
          Author:
          
            
            
              C. J. James
            
          
        
      
          
          Author:
          
            
            
              N. Martini
            
          
        
      
          
          Author:
          
            
            
              D. Menicucci
            
          
        
      
          
          Author:
          
            
            
              A. Gemignani
            
          
        
      
          
          Author:
          
            
            
              B. Ghelarducci
            
          
        
      
          
          Author:
          
            
            
              L. Landini
            
          
        
      
      
      
    
  
   
  
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