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 +6 more sources
A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation [PDF]
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
doaj +5 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 +5 more sources
Constrained Independent Vector Analysis With Reference for Multi-Subject fMRI Analysis. [PDF]
IEEE Trans Biomed Eng, 2023 11 ...
Vu T +4 more
europepmc +6 more sources
A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis [PDF]
Sensors, 2023 Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a ...
Mingyu Sun +6 more
doaj +3 more sources
Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia [PDF]
NeuroImage, 2020 The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations.
Qunfang Long +3 more
doaj +3 more sources
Comparative analysis of group information-guided independent component analysis and independent vector analysis for assessing brain functional network characteristics in autism spectrum disorder [PDF]
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 +2 more sources
Overdetermined Independent Vector Analysis [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources $K$ is less than that of microphones $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that need not to be extracted. Independent vector analysis (IVA) can solve the problem by separating into $M$
Ikeshita, Rintaro +2 more
openaire +4 more sources
Large-Scale Independent Vector Analysis (IVA-G) via Coresets. [PDF]
IEEE Trans Signal ProcessJoint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. “datasets”, into “sources” that are statistically dependent across datasets and independent within datasets.
Gabrielson B +4 more
europepmc +2 more sources
Accelerating Auxiliary Function-Based Independent Vector Analysis [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BSS algorithm, the so-called AuxIVA update rules based on the Majorize-Minimize (MM) principle have been proposed which allow for fast and computationally efficient ...
Brendel, Andreas, Kellermann, Walter
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