Deconvolution of neuronal signal from hemodynamic response

Martin Havlicek, Jiri Jan, Milan Brazdil, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we describe a deconvolution technique for obtaining an approximation of the neuronal signal from an observed hemodynamic response in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Using a series of simulations we show in this paper that we are able to move beyond the limitation of a poorly sampled observation signal and estimate the true structure of underlying neuronal signal with significantly improved temporal resolution.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages617-620
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • cubature Kalman
  • deconvolution
  • fMRI
  • smoother

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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