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» Analysis of Functional Magnetic Resonance Imaging in Python
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ICASSP
2011
IEEE
12 years 11 months ago
Joint blind source separation from second-order statistics: Necessary and sufficient identifiability conditions
This paper considers the problem of joint blind source separation (J-BSS), which appears in many practical problems such as blind deconvolution or functional magnetic resonance im...
Javier Vía, Matthew Anderson, Xi-Lin Li, T&...
MICCAI
2000
Springer
13 years 11 months ago
Retrospective Correction of MR Intensity Inhomogeneity by Information Minimization
In this paper, the problem of retrospective correction of intensity inhomogeneity in magnetic resonance (MR) images is addressed. A novel model-based correction method is proposed,...
Bostjan Likar, Max A. Viergever, Franjo Pernus
NIPS
2007
13 years 9 months ago
Continuous Time Particle Filtering for fMRI
We construct a biologically motivated stochastic differential model of the neural and hemodynamic activity underlying the observed Blood Oxygen Level Dependent (BOLD) signal in Fu...
Lawrence Murray, Amos J. Storkey
HAIS
2008
Springer
13 years 8 months ago
Extracting Multi-knowledge from fMRI Data through Swarm-Based Rough Set Reduction
Abstract. Functional Magnetic Resonance Imaging (fMRI) data is collected ceaselessly during brain research, which implicates some important information. It need to be extracted and...
Hongbo Liu, Ajith Abraham, Hong Ye
ICASSP
2011
IEEE
12 years 11 months ago
Causal signal recovery from U-invariant samples
Causal processing of a signal’s samples is crucial in on-line applications such as audio rate conversion, compression, tracking and more. This paper addresses the problem of cau...
Tomer Michaeli, Yonina C. Eldar, Volker Pohl