In this book, the field of adaptive learning and processing is extended to arguably one of its
most important contexts which is the understanding and analysis of brain signals. No attempt …

Background Brain networks in fMRI are typically identified using spatial independent
component analysis (ICA), yet other mathematical constraints provide alternate biologically …

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to
study spontaneous brain activity. Using rs-fMRI, researchers have extensively studied the …

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 …

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder
that often persists into adulthood. Extracting brain networks from functional magnetic …

Abstract Blind Source Separation (BSS) methods, like Independent Component Analysis
(ICA), show good performance in the analysis of fMRI data. However, the independence …

In this paper the application of Nonnegative Matrix Factorization (NMF) to Functional
Magnetic Resonance Images (fMRIs) is addressed. We attempt to blindly separate the …

In this paper the problem of BOLD detection is addressed. The focus here is on non-
negative matrix factorization (NMF), which is a data driven method and able to provide part …

Functional magnetic resonance imaging (fMRI) data is processed by different techniques for
detection of activated voxels including principal component analysis (PCA), independent …

LANDI, AMANDA KIM. The Nonnegative Matrix Factorization: Methods and Applications. (Under
the direction of Kazufumi Ito Page 1 Abstract LANDI, AMANDA KIM. The Nonnegative Matrix …