In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …

The aim of this review is to provide a comprehensive survey of statistical challenges in
neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging …

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

Canonical correlation analysis (CCA) has been one of the most popular methods for
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …

This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …

Motivation: In biomedical research a growing number of platforms and technologies are
used to measure diverse but related information, and the task of clustering a set of objects …

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI)
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …

In this paper, we propose an analysis mechanism-based structured analysis discriminative
dictionary learning (ADDL) framework. The ADDL seamlessly integrates analysis …