Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Independent Component Analysis

  • Dmitry EfimovEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_147




Independent component analysis


Blind source separation


Probability density function


Cumulative distribution function

EEG Signal

Electroencephalogram signal


Independent component analysis (ICA) (Hyvarinen et al. 2001; Stone 2004) extracts statistically independent variables from a set of measured variables, where each measured variable is affected by a number of underlying physical causes. Extracting such variables is desirable because independent variables are usually generated by different physical processes. Thus, by extracting independent variables, ICA can effectively extract the underlying physical causes for a given set of measured variables.


Most measured quantities are actually mixtures of other quantities. Typical examples are: (a) sound signals in a room with several speakers; (b) an electroencephalogram (EEG) signal, which contains contributions from many...

This is a preview of subscription content, log in to check access.


  1. Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159CrossRefGoogle Scholar
  2. Bell AJ, Sejnowski TJ (1997) The independent components of natural scenes are edge filters. Vis Res 37(23):3327–3338CrossRefGoogle Scholar
  3. Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New YorkCrossRefGoogle Scholar
  4. Makeig S, Jung T, Bell AJ, Ghahremani D, Sejnowski TJ (1997) Blind separation of auditory event-related brain responses into independent components. Proc Natl Acad Sci U S A 94:10979–10984CrossRefGoogle Scholar
  5. McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Sejnowski TJ (1998) Spatially independent activity patterns in functional magnetic resonance imaging data during the stroop color-naming task. Proc Natl Acad Sci U S A 95:803–810CrossRefGoogle Scholar
  6. Stone JV (2004) Independent component analysis: a tutorial introduction. MIT, BostonGoogle Scholar
  7. Van Hateren JH, Van der Schaaf A (1998) Independent component filters of natural images compared with simple cells in primary visual cortex. Proc R Soc Lond B Biol Sci 265(7):359–366CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Mechanics and MathematicsMoscow State UniversityMoscowRussia

Section editors and affiliations

  • Suheil Khoury
    • 1
  1. 1.American University of SharjahSharjahUnited Arab Emirates