Blind signal separation methods in computational neuroscience

Mujahid N. Syed; Pando G. Georgiev; Panos M. Pardalos

doi:10.1007/7657_2013_64

Blind signal separation methods in computational neuroscience

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

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English
Pages (from-to)	291-332
Number of pages	42
Journal	Neuromethods
Volume	91
DOIs	https://doi.org/10.1007/7657_2013_64
State	Published - 2015

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media New York 2013.

Keywords

Blind source separation
Independent component analysis
Sparse component analysis

ASJC Scopus subject areas

General Neuroscience
General Biochemistry, Genetics and Molecular Biology
General Pharmacology, Toxicology and Pharmaceutics
Psychiatry and Mental health

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10.1007/7657_2013_64

Cite this

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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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KW - Independent component analysis

KW - Sparse component analysis

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Blind signal separation methods in computational neuroscience

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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