Blind Source Separation by Entropy Rate Minimization

An algorithm for the blind separation of mutually independent and/or temporally correlated sources is presented in this letter. The algorithm is closely related to the maximum likelihood approach based on entropy rate minimization but uses a simpler contrast function that can be accurately and effic...

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Bibliographic Details
Published in:IEEE signal processing letters 2010-02, Vol.17 (2), p.153-156
Main Authors: Gomez-Herrero, G., Rutanen, K., Egiazarian, K.
Format: Article
Language:English
Subjects:
Blind source separation
Brain modeling
Covariance matrix
Electroencephalography
Entropy
Independent component analysis
Maximum likelihood estimation
Minimization methods
Signal processing algorithms
Source separation
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Summary:An algorithm for the blind separation of mutually independent and/or temporally correlated sources is presented in this letter. The algorithm is closely related to the maximum likelihood approach based on entropy rate minimization but uses a simpler contrast function that can be accurately and efficiently estimated using nearest-neighbor distances. The advantages of the new algorithm are highlighted using simulations and real electroencephalographic data.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2009.2035731