|Reference : Comparison between principal component analysis and independent component analysis in EE...|
|Scientific journals : Article|
|Physical, chemical, mathematical & earth Sciences : Mathematics|
|Comparison between principal component analysis and independent component analysis in EEG modelling|
|Bugli, Céline [ > > ]|
|Lambert, Philippe [Université de Liège - ULg > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales >]|
|Biometrical Journal = Biometrische Zeitschrift|
|Yes (verified by ORBi)|
|[en] Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction
and data reduction, aiming at explaining observed signals as a linear combination of orthogonal
principal components. Independent Component Analysis (ICA) is a technique of array processing and
data analysis, aiming at recovering unobserved signals or ‘sources’ from observed mixtures, exploiting
only the assumption of mutual independence between the signals. The separation of the sources by ICA
has great potential in applications such as the separation of sound signals (like voices mixed in simultaneous
multiple records, for example), in telecommunication or in the treatment of medical signals.
However, ICA is not yet often used by statisticians. In this paper, we shall present ICA in a statistical
framework and compare this method with PCA for electroencephalograms (EEG) analysis.We shall see
that ICA provides a more useful data representation than PCA, for instance, for the representation of a
particular characteristic of the EEG named event-related potential (ERP).
|Fonds National pour la Recherche Scientifique (FNRS)|
|C line Bugli thanks Eli Lilly for financial support through a Mecenat research grant, as well as the FNRS for a research grant.|
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