Late positive event-related potentials enhancement through independent component analysis clustering
Late positive event-related potentials enhancement through independent component analysis clustering
 
  This paper presents a method to evaluate residual dependencies between sources estimated by ICA to be used in a hierarchical clustering procedure. As a proximity measure a mutual information-based metric is employed. The properties of each group of components are evaluated at each level of the hierarchical tree by two indices that aim at assessing both cluster tightness and physiological reliability through a template matching process. These two indices are used in three different approaches to find the most suitable combination to explore the hierarchical structure of the clustering. This method is aimed at enhancing late positive event-related brain potentials elicited by emotional picture stimuli. Such critical brain events are produced by presenting a subject with emotionally arousing images with respect to neutral ones. Exploiting the modularity of the spatial distribution of late EEG components, ICA can be employed to separate out their contribution, that is then investigated in an automatic ad objective manner by the clustering procedure.
  9780863419348
  1-4
  
  
  
    
      Milanesi, M.
      
        df81551a-ce56-4d94-8850-65416b92bb44
      
     
  
    
      James, C.J.
      
        b3733b1f-a6a1-4c9b-b75c-6191d4142e52
      
     
  
    
      Martini, N.
      
        dfe71306-8742-4229-8d34-bcfeb1c0e650
      
     
  
    
      Gemignani, A.
      
        bc8879d9-2063-4b78-a629-80f1a2ea9cb4
      
     
  
    
      Ghelarducci, B.
      
        36ab1580-0afe-455e-b898-e1d04971168a
      
     
  
    
      Menicucci, D.
      
        1d594e51-0536-457b-a1cd-7a7c1767f1f1
      
     
  
    
      Landini, L.
      
        19d472c7-d12d-4674-87f9-6e57c563b553
      
     
  
  
   
  
  
    
      July 2008
    
    
  
  
    
      Milanesi, M.
      
        df81551a-ce56-4d94-8850-65416b92bb44
      
     
  
    
      James, C.J.
      
        b3733b1f-a6a1-4c9b-b75c-6191d4142e52
      
     
  
    
      Martini, N.
      
        dfe71306-8742-4229-8d34-bcfeb1c0e650
      
     
  
    
      Gemignani, A.
      
        bc8879d9-2063-4b78-a629-80f1a2ea9cb4
      
     
  
    
      Ghelarducci, B.
      
        36ab1580-0afe-455e-b898-e1d04971168a
      
     
  
    
      Menicucci, D.
      
        1d594e51-0536-457b-a1cd-7a7c1767f1f1
      
     
  
    
      Landini, L.
      
        19d472c7-d12d-4674-87f9-6e57c563b553
      
     
  
       
    
 
  
    
      
  
  
  
  
    Milanesi, M., James, C.J., Martini, N., Gemignani, A., Ghelarducci, B., Menicucci, D. and Landini, L.
  
  
  
  
   
    (2008)
  
  
    
    Late positive event-related potentials enhancement through independent component analysis clustering.
  
  
  
  
   In 4th IET International Conference on Advances in Medical, Signal and Information Processing, 2008 (MEDSIP 2008). 
  
      IEEE. 
          
          
        .
    
  
  
  
  
  
   
  
    
      Record type:
      Conference or Workshop Item
      (Paper)
      
      
    
   
    
      
        
          Abstract
          This paper presents a method to evaluate residual dependencies between sources estimated by ICA to be used in a hierarchical clustering procedure. As a proximity measure a mutual information-based metric is employed. The properties of each group of components are evaluated at each level of the hierarchical tree by two indices that aim at assessing both cluster tightness and physiological reliability through a template matching process. These two indices are used in three different approaches to find the most suitable combination to explore the hierarchical structure of the clustering. This method is aimed at enhancing late positive event-related brain potentials elicited by emotional picture stimuli. Such critical brain events are produced by presenting a subject with emotionally arousing images with respect to neutral ones. Exploiting the modularity of the spatial distribution of late EEG components, ICA can be employed to separate out their contribution, that is then investigated in an automatic ad objective manner by the clustering procedure.
        
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      Published date: July 2008
 
    
  
  
    
  
    
  
    
     
        Venue - Dates:
        4th IET International Conference on Advances in Medical Signal and Information Processing, 2008 (MEDSIP 2008), Sta Margherita, Italy, 2008-07-14 - 2008-07-16
      
    
  
    
  
    
  
    
  
    
  
    
  
  
  
    
  
  
        Identifiers
        Local EPrints ID: 65183
        URI: http://eprints.soton.ac.uk/id/eprint/65183
        
        
          ISBN: 9780863419348
        
        
        
          PURE UUID: be2db1fc-198e-40c1-a6ef-364e543a7737
        
  
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
        
          
        
    
  
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  Date deposited: 17 Feb 2009
  Last modified: 05 Mar 2024 17:39
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      Contributors
      
          
          Author:
          
            
            
              M. Milanesi
            
          
        
      
          
          Author:
          
            
            
              C.J. James
            
          
        
      
          
          Author:
          
            
            
              N. Martini
            
          
        
      
          
          Author:
          
            
            
              A. Gemignani
            
          
        
      
          
          Author:
          
            
            
              B. Ghelarducci
            
          
        
      
          
          Author:
          
            
            
              D. Menicucci
            
          
        
      
          
          Author:
          
            
            
              L. Landini
            
          
        
      
      
      
    
  
   
  
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