Independent component analysis of human brain waves
Identifieur interne : 000412 ( Main/Exploration ); précédent : 000411; suivant : 000413Independent component analysis of human brain waves
Auteurs : Ricardo Vigário [Finlande] ; Erkki Oja [Finlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1999.
Abstract
Abstract: Recent years have seen a considerable increase in knowledge about the human brain, both in the understanding of some of the most basic human processing systems, and in the eleboration of efficient computational neuroscience models. In a bootstrapping (reinforced) manner, the discoveries made on the human brain are leading into the formulation of more efficient computational methods which in turn make it possible to design new signal processing tools for better extracting information from brain data. In this paper, we will review one of such signal processing tools, the independent component analysis (ICA), that belongs to the class of artificial neural networks. It will be shown how this technique suits the problem of artifact detection, and removal, both in electroencephalographic and magnetoencephalographic recordings. Furthermore, when applied to the evoked field paradigm, ICA separates the complex brain responses into simpler components than the conventional principal component analysis (PCA) approach. This sparse division may lead to an improvement in the interpretation of such event related signals.
Url:
DOI: 10.1007/BFb0100490
Affiliations:
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