49
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Book Chapter: not found
      Advances in Independent Component Analysis 

      The Independence Assumption: Dependent Component Analysis

      other
      Springer London

      Read this book at

      Buy book Bookmark
          There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: not found
          • Article: not found

          An Information-Maximization Approach to Blind Separation and Blind Deconvolution

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Blind separation of auditory event-related brain responses into independent components.

            Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Independent component analysis, A new concept?

                Bookmark

                Author and book information

                Book Chapter
                2000
                : 63-71
                10.1007/978-1-4471-0443-8_4
                2f17f40a-81f3-468e-bbfd-dbb459f93c75
                History

                Comments

                Comment on this book

                Book chapters

                Similar content4,501

                Cited by1