Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data. [PDF]
PLoS ONE, 2017 Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional ...
Moysés Nascimento +8 more
doaj +7 more sources
Independent component analysis: An introduction [PDF]
Applied Computing and Informatics, 2021 Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details.
Alaa Tharwat
doaj +4 more sources
Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry. [PDF]
Talanta, 2020 Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals.
Y. Monakhova, D. Rutledge
semanticscholar +5 more sources
Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA) [PDF]
Journal of Neuroscience Methods, 2017 Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in cognitive neuroscience. They allow for the unique opportunity to record brain activity with a high spatial and temporal resolution.
Sebastian Michelmann +14 more
semanticscholar +4 more sources
Vibration-Based Diagnostics of Rolling Element Bearings Using the Independent Component Analysis (ICA) Method [PDF]
SensorsThis manuscript presents a study on the application of blind source separation (BSS) techniques, specifically the Independent Component Analysis (ICA) method, for the detection and identification of localized faults in rolling element bearings.
Dariusz Mika +2 more
doaj +2 more sources
NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies [PDF]
Frontiers in Neuroinformatics, 2021 Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise ...
Yang Zhao +5 more
doaj +3 more sources
Comparing patterns of component loadings: Principal Component Analysis (PCA) versus Independent Component Analysis (ICA) in analyzing multivariate non-normal data [PDF]
Behavior Research Methods, 2012 Principal component analysis identifies uncorrelated components from correlated variables, and a few of these uncorrelated components usually account for most of the information in the input variables. Researchers interpret each component as a separate entity representing a latent trait or profile in a population. However, the components are guaranteed
Donghoh Kim, Se-Kang Kim
semanticscholar +3 more sources
An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms [PDF]
Tutorials in Quantitative Methods for Psychology, 2010 This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence ...
Dominique Gosselin +2 more
doaj +2 more sources
ICA Model Order Estimation Using Clustering Method [PDF]
Radioengineering, 2007 In this paper a novel approach for independent component analysis (ICA) model order estimation of movement electroencephalogram (EEG) signals is described. The application is targeted to the brain-computer interface (BCI) EEG preprocessing.
P. Sovka, J. Stastny, L. Ruckay
doaj +2 more sources
NeuroImage, 2018 &NA; Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non‐brain source processes and thereby increasing their ...
F. Artoni, A. Delorme, S. Makeig
semanticscholar +3 more sources