Abstract. Functional magnetic resonance imaging (fMRI) is a noninvasive and powerful method for analysis of the operational mechanisms of the brain. fMRI classification poses a sev...
Grid technology can offer a powerful infrastructure for a broad spectrum of (scientific) application areas, but the uptake of grids by "real" applications has been slow....
Abstract. Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented...
Noise confounds present serious complications to accurate data analysis in functional magnetic resonance imaging (fMRI). Simply relying on contextual image information often resul...
Abstract. Over the last few years, functional Magnetic Resonance Imaging (fMRI) has emerged as a new and powerful method to map the cognitive states of a human subject to specific...
Diego Sona, Sriharsha Veeramachaneni, Emanuele Oli...
Abstract. In contrast to the traditional hypothesis-driven methods, independent component analysis (ICA) is commonly used in functional magnetic resonance imaging (fMRI) studies to...
Image analysis is an important component of neuroscience research. The ICT infrastructure and technical knowledge needed to perform (large scale) neuroimaging studies, however, is...
— Non-negative Matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last couple of years. NMF, like independent component analysis (ICA) is...
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide complementary information about the brain function. We propose a novel scheme to examine asso...
Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of signals. We propose an improved ICA framework ...
Jing Sui, Jingyu Liu, Lei Wu, Andrew Michael, Lai ...