Abstract
In this paper we present a survey of Blind Signal Separation (BSS) methods based on independence (Independent Component Analysis) and sparsity (Sparse Component Analysis). The presentation covers the most important methods described in the literature and gives a mathematical justification of the most used algorithms. We provide an experimental justification for the linear mixing in neurological data. Furthermore, we show the applicability of nonnegative source decomposition approaches in demixing neural images.
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Acknowledgements
Research has been partially supported by DTRA, Air Force and NSF grants. The last author is also partially supported by LATNA Laboratory, NRU HSE, RF government grant, ag. 11.G34.31.0057.
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Syed, M.N., Georgiev, P.G., Pardalos, P.M. (2013). Blind Signal Separation Methods in Computational Neuroscience. In: Sakkalis, V. (eds) Modern Electroencephalographic Assessment Techniques. Neuromethods, vol 91. Humana Press, New York, NY. https://doi.org/10.1007/7657_2013_64
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DOI: https://doi.org/10.1007/7657_2013_64
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