Paper Info
Title
A Comparison of ICA Algorithms in Biomedical Signal Processing.
Abstract
In the last years Independent Component Analysis (ICA) has been applied with success in signal processing and many algorithms have been developed in order to perform ICA. In this paper we review some algorithms, like INFOMAX (Bell and Sejnowski 1995), extended-INFOMAX (Lee, Girolami and Sejniowski 1997), FastICA (OjA, and Hyvarinen 1999), that solve the ICA problem under the assumption of the linear mixture model. We also show an overview of the nonlinear ICA algorithms and we discuss the MISEP (Almeida 2003). In order to test the performances of the reviewed algorithms, we present some applications of ICA in biomedical signal processing. In particular the application of ICA to the electroencephalographic (EEG) and surface electromyographic (sEMG) recordings are shown.
Year
DOI
Venue
2004
10.1007/1-4020-3432-6_36
BIOLOGICAL AND ARTIFICIAL INTELLIGENCE ENVIRONMENTS
Keywords
Field
DocType
Independent Component Analysis,Neural Networks,Artifact Removal,sEMG,EEG,Biomedical Signals
Signal processing,Nonlinear system,Pattern recognition,Computer science,Algorithm,Independent component analysis,Artificial intelligence,FastICA,Artificial neural network,Infomax,Electroencephalography,Mixture model
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Bruno Azzerboni1255.31
Maurizio Ipsale272.49
Fabio La Foresta39315.69
Nadia Mammone413619.69
Francesco Carlo Morabito533954.83