With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …

In various disciplines, information about the same phenomenon can be acquired from
different types of detectors, at different conditions, in multiple experiments or subjects …

It is becoming increasingly clear that combining multimodal brain imaging data provides
more information for individual subjects by exploiting the rich multimodal information that …

Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …

Operations with tensors, or multiway arrays, have become increasingly prevalent in recent
years. Traditionally, tensors are represented or decomposed as a sum of rank-1 outer …

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG
signals tend to be represented by a vector or a matrix to facilitate data processing and …

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships
between measures of brain activity and of behavior or experimental design. In …

This survey provides an overview of higher-order tensor decompositions, their applications,
and available software. A tensor is a multidimensional or N-way array. Decompositions of …

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain.
It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) …

Inferring resting-state connectivity patterns from functional magnetic resonance imaging
(fMRI) data is a challenging task for any analytical technique. In this paper, we review a …