Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity of the analysis both for data driven technique...
We propose a method for reconstruction of human brain states directly from functional neuroimaging data. The method extends the traditional multivariate regression analysis of dis...
Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W....
Over the past decade functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cogni...
Vishwajeet Singh, Krishna P. Miyapuram, Raju S. Ba...
We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods that are increasing...
Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distr...
Jon M. Fincham, John R. Anderson, Shawn Betts, Jen...
An adaptive filtering method for fMRI data is presented. The method is related to bilateral filtering, but with a range filter that takes into account local similarities in sig...
Large intersubject variability is a well-described feature of fMRI studies, making inter-group inference, of critical importance for biological interpretation, difficult. Therefor...
Martin J. McKeown, Junning Li, Xuemei Huang, Z. Ja...
Abstract. We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional repr...
Abstract For many centuries scientists have wondered how the human brain represents thoughts in terms of the underlying biology of neural activity. Philosophers, linguists, cogniti...
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study the brai...