Results 61 to 70 of about 265,367 (299)
Efficient independent component analysis
, 2007 Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication.
Bickel, Peter J., Chen, Aiyou
core +2 more sources
Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani +10 more
wiley +1 more source
Unique estimation in EEG analysis by the ordering ICA
PLoS ONE, 2022 Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG ...
Yoshitatsu Matsuda, Kazunori Yamaguchi
doaj
Factor analysis of financial time series using EEMD-ICA based approach
Sustainable Futures, 2020 Analyses of financial time series and exploring its underlying characteristic factors are longstanding research problems. Ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA) are two methods developed to deal with these ...
Lu Xian +3 more
doaj +1 more source
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series [PDF]
, 2009 Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information.
Poline, Jean Baptiste +3 more
core +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Collaterals are crucial factors that influence the infarct growth rate (IGR). We aimed to determine whether a comprehensive multimodal collateral score (MCS), incorporating collateral assessment at the arterial, tissue, and venous levels, is associated with functional independence and provides incremental prognostic value over ...
Giorgio Busto +12 more
wiley +1 more source
Jixie chuandong, 2015 The method of independent component analysis is widely used in mechanical equipment fault diagnosis domain.A novel method named as constrained robust independent component analysis（ cRobust ICA）based on Robust ICA algorithm is proposed which utilized ...
Liao Qiang, Li Xunbo, Huang Bo
doaj
Advanced Healthcare Materials, EarlyView.Implantable optoelectrical devices are an effective resource for the modulation and monitoring of neural activity with high spatiotemporal resolution. This review discusses current challenges faced by these devices and outlines future perspectives for the development of next‐generation neural interfaces targeting chronic, multisite, and multimodal ...
Stella Aslanoglou +4 more
wiley +1 more source
Detection of functional networks within white matter using independent component analysis
NeuroImage, 2020 Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity.
Yali Huang +7 more
semanticscholar +1 more source
Independent component analysis for brain FMRI does indeed select for maximal independence. [PDF]
PLoS ONE, 2013 A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence.
Vince D Calhoun +7 more
doaj +1 more source