WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis
Background Researches declared that the super-Gaussian property contributed to the
success of some spatial independent component analysis (ICA) algorithms in brain fMRI …
success of some spatial independent component analysis (ICA) algorithms in brain fMRI …
SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis
Independent component analysis (ICA) has been widely used in functional magnetic
resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that …
resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that …
Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources
R Ge, Y Wang, J Zhang, L Yao, H Zhang… - Journal of neuroscience …, 2016 - Elsevier
Background As a blind source separation technique, independent component analysis (ICA)
has many applications in functional magnetic resonance imaging (fMRI). Although either …
has many applications in functional magnetic resonance imaging (fMRI). Although either …
Improving Source Separation for Multi-subject fMRI Data by Incorporating Signal Intensity and Spatiotemporal Basis Expansion
MU Khalid, MM Nauman, PMIB Pg Hj Petra… - Proceedings of the 2024 …, 2024 - dl.acm.org
Recently, a sparse spatiotemporal blind source separation (ssBSS) algorithm was proposed
to perform functional magnetic resonance imaging (fMRI) data analysis as a potential …
to perform functional magnetic resonance imaging (fMRI) data analysis as a potential …
Performance of blind source separation algorithms for fMRI analysis using a group ICA method
Independent component analysis (ICA) is a popular blind source separation technique that
has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) …
has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) …
Temporally constrained ICA with threshold and its application to fMRI data
Z Long, Z Wang, J Zhang, X Zhao, L Yao - BMC Medical Imaging, 2019 - Springer
Background Although independent component analysis (ICA) has been widely applied to
functional magnetic resonance imaging (fMRI) data to reveal spatially independent brain …
functional magnetic resonance imaging (fMRI) data to reveal spatially independent brain …
Source density‐driven independent component analysis approach for fMRI data
B Hong, GD Pearlson, VD Calhoun - Human brain mapping, 2005 - Wiley Online Library
Independent component analysis (ICA) has become a popular tool for functional magnetic
resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax …
resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax …
Automatic identification of functional clusters in FMRI data using spatial dependence
In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI)
data, extracting a large number of maximally independent components provides a detailed …
data, extracting a large number of maximally independent components provides a detailed …
Consistency of independent component analysis for FMRI
Background Independent component analysis (ICA) has been widely used for blind source
separation in the field of medical imaging. However, despite of previous substantial efforts …
separation in the field of medical imaging. However, despite of previous substantial efforts …
Gaussian process based independent analysis for temporal source separation in fMRI
Abstract Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the
processes of the brain, and opens up for analyzing the functional activation patterns of the …
processes of the brain, and opens up for analyzing the functional activation patterns of the …