Results 1 to 10 of about 69 (63)
A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation
Sensors, 2023 With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive. It has become extremely important to extract useful signals by decomposing various mixed signals through blind source
Ruiming Guo, Zhongqiang Luo, Mingchun Li
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Independent vector analysis (IVA): Multivariate approach for fMRI group study
NeuroImage, 2008 Independent component analysis (ICA) of fMRI data generates session/individual specific brain activation maps without a priori assumptions regarding the timing or pattern of the blood-oxygenation-level-dependent (BOLD) signal responses. However, because of a random permutation among output components, ICA does not offer a straightforward solution for ...
Jong-Hwan Lee +3 more
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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 +10 more
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Large-Scale Independent Vector Analysis (IVA-G) via Coresets
IEEE Transactions on Signal ProcessingJoint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. 揹atasets , into 搒ources that are statistically dependent across datasets and independent within datasets. Despite this usefulness for analyzing multiple datasets, JBSS methods suffer from considerable computational costs and are typically intractable for ...
Ben Gabrielson +4 more
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Fusion of Multi-Task fMRI Data: Guided Solutions for IVA and Transposed IVA
SensorsIndependent vector analysis (IVA) has emerged as a powerful tool for fusing and analyzing functional magnetic resonance imaging (fMRI) data. Applying IVA to multi-task fMRI data enhances analytical power by capturing the relationships across different ...
Emin Erdem Kumbasar +3 more
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Fasteriva: Update Rules for Independent Vector Analysis Based on Negentropy and the Majorize-Minimize Principle [PDF]
2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021 Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and ...
Andreas Brendel, Walter Kellermann
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Isolation of multiple electrocardiogram artifacts using independent vector analysis [PDF]
PeerJ Computer Science, 2023 Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove.
Zahoor Uddin +4 more
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Frontiers in Neuroscience, 2023 IntroductionGroup information-guided independent component analysis (GIG-ICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies.
Junlin Jing +5 more
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Independent Vector Analysis Inspired Amateur Drone Detection Through Acoustic Signals
IEEE Access, 2021 Detection of amateur drones (AmDrs) is mandatory requirement of various defence organizations and is also required to protect human life. In literature, various researchers contributed in this regard and developed different algorithms utilizing video ...
Zahoor Uddin +3 more
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Independent vector analysis (IVA) for group fMRI processing of subcortical area [PDF]
International Journal of Imaging Systems and Technology, 2008 AbstractDuring functional MRI (fMRI) studies, blood oxygenation‐level dependent (BOLD) signal associated with neuronal activity acquired from multiple individuals are subject to the derivation of group‐averaged brain activation patterns. Unlike other cortical areas, subcortical areas such as the thalamus and basal ganglia often manifest smaller ...
Jong-Hwan Lee +3 more
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