BSS and ICA in neuroinformatics: from current practices to open challenges

IEEE Rev Biomed Eng. 2008:1:50-61. doi: 10.1109/RBME.2008.2008244.


We give a general overview of the use and possible misuse of blind source separation (BSS) and independent component analysis (ICA) in the context of neuroinformatics data processing. A clear emphasis is given to the analysis of electrophysiological recordings, as well as to functional magnetic resonance images (fMRI). Two illustrative examples include the identification and removal of artefacts in both kinds of data, and the analysis of a simple fMRI. A second part of the paper addresses a set of currently open challenges in signal processing. These include the identification and analysis of independent subspaces, the study of networks of functional brain activity, and the analysis of single-trial event-related data.

Publication types

  • Review

MeSH terms

  • Animals
  • Computational Biology / methods*
  • Computational Biology / trends
  • Electroencephalography / methods*
  • Electroencephalography / trends
  • Humans
  • Magnetic Resonance Imaging / methods*
  • Magnetic Resonance Imaging / trends
  • Models, Neurological*
  • Signal Processing, Computer-Assisted*