Blind Signal Separation from Optical Imaging Data

Ingo Schießl; Martin Stetter; John E. W. Mayhew; Niall McLoughlin; Jenny S. Lund; Klaus Obermayer

doi:10.1007/978-3-642-59802-9_12

Blind Signal Separation from Optical Imaging Data

Ingo Schießl, Martin Stetter, John E. W. Mayhew, Niall McLoughlin, Jenny S. Lund, Klaus Obermayer

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Optical imaging is the video recording of two-dimensional patterns of changes in light reflectance from cortical tissue evoked by Stimulation. We derived a method, called extended spatial decorrelation (ESD), that uses second order statistics in space for separating the intrinsic signals into the stimulus related components and the nonspecific variations. The Performance of ESD on model data is compared to independent component analysis (ICA) algorithms using statistics of 4th and higher order. Robustness against sensor noise is scored. When applied to optical images, ESD separates the stimulus specific signal well from biological noise and artifacts.

Original language	Undefined
Title of host publication	Mustererkennung 2000 22. DAGM-Symposium. Kiel, 13.–15. September 2000
Editors	Gerald Sommer , Norbert Krüger , Christian Perwass
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-59802-9_12
Publication status	Published - 2000

Access to Document

10.1007/978-3-642-59802-9_12

Cite this

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title = "Blind Signal Separation from Optical Imaging Data",

abstract = "Optical imaging is the video recording of two-dimensional patterns of changes in light reflectance from cortical tissue evoked by Stimulation. We derived a method, called extended spatial decorrelation (ESD), that uses second order statistics in space for separating the intrinsic signals into the stimulus related components and the nonspecific variations. The Performance of ESD on model data is compared to independent component analysis (ICA) algorithms using statistics of 4th and higher order. Robustness against sensor noise is scored. When applied to optical images, ESD separates the stimulus specific signal well from biological noise and artifacts.",

author = "Ingo Schie{\ss}l and Martin Stetter and Mayhew, {John E. W.} and Niall McLoughlin and Lund, {Jenny S.} and Klaus Obermayer",

year = "2000",

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language = "Undefined",

isbn = "9783540678861",

editor = "{ Gerald Sommer} and {Norbert Kr{\"u}ger} and {Christian Perwass}",

booktitle = "Mustererkennung 2000 22. DAGM-Symposium. Kiel, 13.–15. September 2000",

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T1 - Blind Signal Separation from Optical Imaging Data

AU - Schießl, Ingo

AU - Stetter, Martin

AU - Mayhew, John E. W.

AU - McLoughlin, Niall

AU - Lund, Jenny S.

AU - Obermayer, Klaus

PY - 2000

Y1 - 2000

N2 - Optical imaging is the video recording of two-dimensional patterns of changes in light reflectance from cortical tissue evoked by Stimulation. We derived a method, called extended spatial decorrelation (ESD), that uses second order statistics in space for separating the intrinsic signals into the stimulus related components and the nonspecific variations. The Performance of ESD on model data is compared to independent component analysis (ICA) algorithms using statistics of 4th and higher order. Robustness against sensor noise is scored. When applied to optical images, ESD separates the stimulus specific signal well from biological noise and artifacts.

AB - Optical imaging is the video recording of two-dimensional patterns of changes in light reflectance from cortical tissue evoked by Stimulation. We derived a method, called extended spatial decorrelation (ESD), that uses second order statistics in space for separating the intrinsic signals into the stimulus related components and the nonspecific variations. The Performance of ESD on model data is compared to independent component analysis (ICA) algorithms using statistics of 4th and higher order. Robustness against sensor noise is scored. When applied to optical images, ESD separates the stimulus specific signal well from biological noise and artifacts.

U2 - 10.1007/978-3-642-59802-9_12

DO - 10.1007/978-3-642-59802-9_12

M3 - Conference contribution

SN - 9783540678861

BT - Mustererkennung 2000 22. DAGM-Symposium. Kiel, 13.–15. September 2000

A2 - , Gerald Sommer

A2 - , Norbert Krüger

A2 - , Christian Perwass

ER -