Results 31 to 40 of about 27,806 (135)

Joint Independent Subspace Analysis: A Quasi-Newton Algorithm [PDF]

, 2015
International audienceIn this paper, we present a quasi-Newton (QN) algorithm for joint independent subspace analysis (JISA). JISA is a recently proposed generalization of independent vector analysis (IVA). JISA extends classical blind source separation (
Jutten, Christian, Lahat, Dana
core   +3 more sources

Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data

Frontiers in Neuroscience, 2017
Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as ...
Yuhui Du, Yuhui Du, Dongdong Lin, Qingbao Yu, Jing Sui, Jing Sui, Jiayu Chen, Srinivas Rachakonda, Tulay Adali, Vince D. Calhoun, Vince D. Calhoun   +10 more
doaj   +1 more source

Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data

Sensors, 2022
It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions.
M. A. B. S. Akhonda, Yuri Levin-Schwartz, Vince D. Calhoun, Tülay Adali   +3 more
doaj   +1 more source

Preserving Subject Variability in Group fMRI Analysis: Performance Evaluation of GICA versus IVA

Frontiers in Systems Neuroscience, 2014
Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI ...
Andrew eMichael, Andrew eMichael, Mathew eAnderson, Robyn eMiller, Tülay eAdalı, Vince D Calhoun, Vince D Calhoun   +6 more
doaj   +1 more source

Auxiliary-Function-Based Independent Vector Analysis Using Generalized Inter-Clique Dependence Source Models With Clique Variance Estimation

IEEE Access, 2020
By introducing a frequency dependence source prior including full-band and clique models, independent vector analysis (IVA) has been successfully used for convolutive blind source separation (BSS). In addition, independent low-rank matrix analysis (ILRMA)
Ui-Hyeop Shin, Hyung-Min Park
doaj   +1 more source

Blind source separation‐based IVA‐Xception model for bird sound recognition in complex acoustic environments

Electronics Letters, 2021
Identification of bird species from audio recordings has been a major area of interest within the field of ecological surveillance and biodiversity conservation. Previous studies have successfully identified bird species from given recordings.
Yusheng Dai, Jin Yang, Yiwei Dong, Haipeng Zou, Mingzhi Hu, Bin Wang   +5 more
doaj   +1 more source

Independent vector analysis based on overlapped cliques of variable width for frequency-domain blind signal separation [PDF]

, 2012
A novel method is proposed to improve the performance of independent vector analysis (IVA) for blind signal separation of acoustic mixtures. IVA is a frequency-domain approach that successfully resolves the well-known permutation problem by applying a ...
Jang, Gil-Jin, Lee, Intae
core   +1 more source

An Acoustic Signal Enhancement Method Based on Independent Vector Analysis for Moving Target Classification in the Wild

Sensors, 2017
In this paper, we study how to improve the performance of moving target classification by using an acoustic signal enhancement method based on independent vector analysis (IVA) in the unattended ground sensor (UGS) system.
Qin Zhao, Feng Guo, Xingshui Zu, Yuchao Chang, Baoqing Li, Xiaobing Yuan   +5 more
doaj   +1 more source

Constrained Independent Vector Analysis With Reference for Multi-Subject fMRI Analysis. [PDF]

IEEE Trans Biomed Eng
Vu T, Laport F, Yang H, Calhoun VD, Adal T.   +4 more
europepmc   +2 more sources

Auxiliary function based IVA using a source prior exploiting fourth order relationships

, 2013
International audienceIndependent vector analysis (IVA) can theoretically avoid the permutation ambiguity present in frequency domain independent component analysis by using a multivariate source prior to retain the dependency between different frequency
Chambers, Jonathon, Chen, Gaojie, Harris, Jack, Jutten, Christian, Liang, Yanfeng, Mohsen Naqvi, Syed   +5 more
core   +2 more sources