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Blind Signal Separation Methods in Computational Neuroscience

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Book cover Modern Electroencephalographic Assessment Techniques

Part of the book series: Neuromethods ((NM,volume 91))

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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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Notes

  1. 1.

    http://wiki.neurotycho.org/EEG-ECoG_recording.

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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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Authors and Affiliations

  1. Center for Applied Optimization, University of Florida, Gainesville, FL, 32611, USA

    Mujahid N. Syed, Pando G. Georgiev & Panos M. Pardalos

Authors
  1. Mujahid N. Syed
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  2. Pando G. Georgiev
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  3. Panos M. Pardalos
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Corresponding author

Correspondence to Mujahid N. Syed .

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Editors and Affiliations

  1. Foundation for Research and Technology, Heraklion, Crete, Greece

    Vangelis Sakkalis

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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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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1297-1

  • Online ISBN: 978-1-4939-1298-8

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