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
openaire +3 more sources
Fusion of Multi-Task fMRI Data: Guided Solutions for IVA and Transposed IVA [PDF]
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
doaj +2 more sources
Large-Scale Independent Vector Analysis (IVA-G) via Coresets [PDF]
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
openaire +2 more sources
Independent Vector Analysis for Feature Extraction in Motor Imagery Classification [PDF]
SensorsIndependent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information.
Caroline Pires Alavez Moraes +4 more
doaj +2 more sources
Multidimensional Comparisons Between Constrained ICA/IVA Algorithms for Multi-Subject fMRI Data Analysis [PDF]
IEEE AccessLarge-scale functional magnetic resonance imaging (fMRI) datasets provide exciting opportunities for understanding and improving brain health. Data-driven techniques such as independent component analysis (ICA) and independent vector analysis (IVA) have ...
Lucas Gois +7 more
doaj +2 more sources
Drone-assisted time-varying magnetic field analysis for fault diagnosis in grounding grids. [PDF]
PLoS ONEGrounding grids are essential for ensuring the safety of power substations, but their performance can degrade due to corrosion, fractures, or other faults.
Aamir Qamar, Zahoor Uddin
doaj +2 more sources
Identifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data [PDF]
IEEE AccessIdentifying relationships among multiple datasets is an effective way to summarize information and has been growing in importance. In this paper, we propose a robust 3-step method for identifying the relationship structure among multiple datasets based ...
Isabell Lehmann +5 more
doaj +2 more sources
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 ...
Brendel, Andreas, Kellermann, Walter
openaire +2 more sources
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
doaj +2 more sources
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
doaj +1 more source