Analyzing Brain Tumor related EEG Signals with ICA Algorithms

Habl, M.; Bauer, C.; Ziegaus, Christian; Lang, Elmar; Schulmeyer, F.

doi:10.1007/978-1-4471-0513-8_18

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…A common application of ICA is in blind source separation (BSS) problems, for example of EEG [27], [14], MEG [41] and fMRI [42], [19] data. ICA can also be used to reduce noise [40], [35], [12] or artifacts [36,37] if generated from independent sources.…”

Section: Matrix Decomposition Methodsmentioning

confidence: 99%

Hybridizing sparse component analysis with genetic algorithms for microarray analysis

et al. 2008

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Hybridizing sparse component analysis with genetic algorithms for microarray analysis, Neurocomputing (2008), doi:10.1016/j.neucom.2007.09.017 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A c c e p t e d m a n u s c r i p t AbstractNonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to Blind Source Separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently sparse. In contrast to most well-established BSS methods, the devised algorithm is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a genetic algorithm for its minimization. Finally, we apply the devised algorithm to real world microarray data.

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