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Analysing EEG brain signals using independent component analysis techniques [PDF]
, 2011 The use of electroencephalography (EEG) in the medical field is evident in the effect it has on diagnosis and treatment of patients who suffer from some form of brain problem. These signals however once collected are overlayed with artifacts. This thesis
Williams, Janett G. St. H.
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TFT-LCD Mura Defects Using Independent Component Analysis
Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2009 Independent component analysis (ICA) is used to detect the mura regions with varying sizes and brightness levels before thresholding, then individually analyzed the mura regions in order to avoid unnecessary background effect.
Shang Liang CHEN, Chi Chin YANG
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Heavy-Tailed Independent Component Analysis [PDF]
2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 2015 Independent component analysis (ICA) is the problem of efficiently recovering a matrix $A \in \mathbb{R}^{n\times n}$ from i.i.d. observations of $X=AS$ where $S \in \mathbb{R}^n$ is a random vector with mutually independent coordinates. This problem has been intensively studied, but all existing efficient algorithms with provable guarantees require ...
Joseph Anderson +3 more
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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
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Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal
Mathematics, 2021 Motivated by mapping adverse artifactual events caused by body movements in electroencephalographic (EEG) signals, we present a functional independent component analysis based on the spectral decomposition of the kurtosis operator of a smoothed principal
Marc Vidal +2 more
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Independent Component Analysis for Magnetic Resonance Image Analysis
EURASIP Journal on Advances in Signal Processing, 2008 Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number ...
San-Kan Lee +7 more
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Human locomotion-control brain networks detected with independent component analysis [PDF]
Journal of Integrative Neuroscience, 2021 Walking is a fundamental movement skill in humans. However, how the brain controls walking is not fully understood. In this functional magnetic resonance imaging study, the rhythmic, bilaterally alternating ankle movements were used as paradigm to ...
Pengxu Wei +3 more
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Noisy independent component analysis of autocorrelated components [PDF]
Physical Review E, 2017 We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the
Knollmüller, J., Enßlin, T.
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Frontiers in Human Neuroscience, 2018 Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes ...
Marcel Prestel +4 more
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Consistency of independent component analysis for FMRI
Journal of Neuroscience Methods, 2021 Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts.
Zhao, Wei +8 more
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