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Proceedings Paper

Geometric methods in nonlinear analysis of data from brain imaging
Author(s): Hamid Eghbalnia; Amir H. Assadi
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Paper Abstract

The aims of this series of papers are: (a) to formulate a geometric framework for non-linear analysis of global features of massive data sets; and (b) to quantify non-linear dependencies among (possibly) uncorrelated parameters that describe the data. In this paper, we consider an application of the methods to extract and characterize nonlinearities in the functional magnetic resonance imaging data and EEG of human brain (fMRI). A more general treatment of this theory applies to a wider variety of massive data sets; however, the usual technicalities for computation and accurate interpretation of abstract concepts remain a challenge for each individual area of application.

Paper Details

Date Published: 2 November 2001
PDF: 9 pages
Proc. SPIE 4476, Vision Geometry X, (2 November 2001); doi: 10.1117/12.447277
Show Author Affiliations
Hamid Eghbalnia, Univ. of Wisconsin/Madison (United States)
Amir H. Assadi, Univ. of Wisconsin/Madison (United States)

Published in SPIE Proceedings Vol. 4476:
Vision Geometry X
Longin Jan Latecki; David M. Mount; Angela Y. Wu; Robert A. Melter, Editor(s)

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