A theoretical analysis of an alternative CUSUM statistic called CUSUM-slope for detecting signals from background noise in a low signal-to-noise environment

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Article accompanying a poster presentation for the 2009 Computational Neuroscience Meeting. This article discusses a theoretical analysis of an alternative CUSUM statistic called CUSUM-slope for detecting signals from background noise in a low signal-to-noise environment.

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Tam, Nicoladie D. July 13, 2009.

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Article accompanying a poster presentation for the 2009 Computational Neuroscience Meeting. This article discusses a theoretical analysis of an alternative CUSUM statistic called CUSUM-slope for detecting signals from background noise in a low signal-to-noise environment.

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1 p.

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Abstract: Signal detection in a low signal-to-noise environment, such as signals recorded from fMRI or EEG, is often a daunting task. In this paper, the authors limit the definition of noise to events that are generated from a random process whereas signal is defined as events that are generated from a non-random process. This paper introduces an alternate method, called CUSUM-slope, in which the "slope" of the CUSUM-curve is used as an approximation of the discrete-time-derivative of the CUSUM curve to provide time-independence and origin-independence of the analysis.

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  • Eighteenth Annual Computational Neuroscience Meeting: CNS, 2009, Berlin, Germany

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  • Publication Title: BMC Neuroscience
  • Volume: 10
  • Issue: Suppl 1
  • Peer Reviewed: Yes

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  • July 13, 2009

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  • Nov. 30, 2012, 9:15 a.m.

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  • May 27, 2014, 2:12 p.m.

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Tam, Nicoladie D. A theoretical analysis of an alternative CUSUM statistic called CUSUM-slope for detecting signals from background noise in a low signal-to-noise environment, article, July 13, 2009; [London, United Kingdom]. (digital.library.unt.edu/ark:/67531/metadc122148/: accessed June 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.