Localization of brain activity — blind separation for fMRI data

Silke Dodel; J.Michael Herrmann; Theo Geisel

doi:10.1016/S0925-2312(00)00235-6

Localization of brain activity — blind separation for fMRI data

Silke Dodel, J.Michael Herrmann, Theo Geisel

School of Informatics

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

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, e.g. in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.

Original language	English
Pages (from-to)	701-708
Number of pages	8
Journal	Neurocomputing
Volume	32–33
DOIs	https://doi.org/10.1016/S0925-2312(00)00235-6
Publication status	Published - Jun 2000

Keywords

Functional magnetic resonance imaging (fMRI)
Principal component analysis (PCA)
Independent component analysis (ICA)

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10.1016/S0925-2312(00)00235-6

http://www.sciencedirect.com/science/article/pii/S0925231200002356

Cite this

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title = "Localization of brain activity — blind separation for fMRI data",

abstract = "Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, e.g. in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.",

keywords = "Functional magnetic resonance imaging (fMRI), Principal component analysis (PCA), Independent component analysis (ICA)",

author = "Silke Dodel and J.Michael Herrmann and Theo Geisel",

year = "2000",

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doi = "10.1016/S0925-2312(00)00235-6",

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AU - Dodel, Silke

AU - Herrmann, J.Michael

AU - Geisel, Theo

PY - 2000/6

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N2 - Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, e.g. in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.

AB - Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, e.g. in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.

KW - Functional magnetic resonance imaging (fMRI)

KW - Principal component analysis (PCA)

KW - Independent component analysis (ICA)

U2 - 10.1016/S0925-2312(00)00235-6

DO - 10.1016/S0925-2312(00)00235-6

M3 - Article

VL - 32–33

SP - 701

EP - 708

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -

Localization of brain activity — blind separation for fMRI data

Abstract

Keywords

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