Abstract
An efficient Stone’s BSS (ESBSS) algorithm is proposed based on the joint between original Stone’s BSS (SBSS) and genetic algorithm (GA). Original Stone’s BSS has several advantages compared with independent component analysis (ICA) techniques, where the BSS problem in Stone’s BSS is simplified to generalized eigenvalue decomposition (GEVD), but it’s susceptible to the local minima problem. Therefore, GA is used to overcome this problem and to enhance the separation process. Performance of the proposed algorithm is first tested through a different pdf source, followed by artifact extraction test for EEG mixtures then compared with the original Stone’s BSS (SBSS) and other BSS algorithms. The results demonstrate proposed algorithm efficiency in real time blind extraction of both superGaussian and sub-Gaussian signals from their mixtures.
Published Version
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