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This paper addresses the blind source separation (BSS) problem where the input data are mixtures of sources that are “sparse”—that is each source has zero amplitude for some of the time. It is shown that, under certain conditions, it is possible to separate these sources by the analysis of localised segments of the phase space trajectory where one source dominates rather than applying statistical...
An important trend in medical technology is towards support for personalised healthcare, fuelled by developments in genomic-based medicine. New computational intelligent techniques for biodata analysis will be needed to fully exploit the vast amounts of data that are being generated. Non-linear signal processing methods will form an important part of such computational intelligent techniques. This...
The paper discusses methods for independent source identification within multiple channels electroencephalographical (EEG) signals recordings. The focus is to compare the independent component analysis (ICA) technique to a novel proposed method for individual components separation - the phase space method (PSM). Methods are suitable to be used for any multi-lead signal especially within biomedical...
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