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The quality of the reconstructed sources is quantified by two measures, one based on a distance measure implemented by a Frobenius norm, and one based on residual mutual information. We find that the IC\u2013LSS modeling algorithm offers several advantages over the ICA algorithms: it succeeds in unmixing Gaussian sources, on short time series it performs, on average, better than static ICA algorithms, it does not try to remove coincidental dependencies resulting from finite data set size, and it shows the potential to reconstruct the sources even in the case of noninvertible mixing. As expected, for the case of non-Gaussian sources, invertible mixing and sufficient time series length, the ICA algorithms typically outperform IC\u2013LSS modeling. <\/jats:p>","DOI":"10.1142\/s0218127413501654","type":"journal-article","created":{"date-parts":[[2013,10,29]],"date-time":"2013-10-29T23:12:20Z","timestamp":1383088340000},"page":"1350165","source":"Crossref","is-referenced-by-count":3,"title":["BLIND SIGNAL SEPARATION OF MIXTURES OF CHAOTIC PROCESSES: A COMPARISON BETWEEN INDEPENDENT COMPONENT ANALYSIS AND STATE SPACE MODELING"],"prefix":"10.1142","volume":"23","author":[{"given":"ANDREAS","family":"GALKA","sequence":"first","affiliation":[{"name":"Department of Neuropediatrics, University of Kiel, 24098 Kiel, Germany"},{"name":"Institute of Experimental and Applied Physics, University of Kiel, 24098 Kiel, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KEVIN K. 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