ASSISTED DICTIONARY LEARNING FOR FMRI DATA ANALYSIS

被引：0

作者：

Moreno, Manuel Morante ^{[1
,2
]}

Kopsinis, Yannis ^{[2
,3
]}

Kofidis, Eleftherios ^{[2
,4
]}

Chatzichristos, Christos ^{[1
,2
]}

Theodoridis, Sergios ^{[1
,2
,5
]}

机构：

[1] Univ Athens, Dept Informat & Telecommun, Athens, Greece

[2] Comp Technol Inst & Press Diophantus CTI, Patras, Greece

[3] LIBRA MLI Ltd, Edinburgh, Midlothian, Scotland

[4] Univ Piraeus, Dept Stat & Insurance Sci, Piraeus, Greece

[5] Natl Observ Athens, IAASARS, GR-15236 Penteli, Greece

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年

基金：

欧盟地平线“2020”;

关键词：

fMRI Data Analysis; Dictionary Learning; Blind Source Separation; INDEPENDENT COMPONENT ANALYSIS; BOLD HEMODYNAMIC-RESPONSES; FUNCTIONAL CONNECTIVITY; VARIABILITY; SEPARATION; SIMULATION; TOOLBOX; ICA;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Extracting information from functional magnetic resonance images (fMRI) has been a major area of research for more than two decades. The goal of this work is to present a new method for the analysis of fMRI data sets, that is capable to incorporate a priori available information, via an efficient optimization framework. Tests on synthetic data sets demonstrate significant performance gains over existing methods of this kind.

引用

页码：806 / 810

页数：5

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