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 …

In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for
improving classification accuracy and increasing the number of commands are reviewed …

In recent days, the incidence of Diabetic Retinopathy (DR) has become high, affecting the
eyes because of drastic increase in the glucose level in blood. Globally, almost half of the …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …

The brain disorders may cause loss of some critical functions such as thinking, speech, and
movement. So, the early detection of brain diseases may help to get the timely best …

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 …

Tensor networks have in recent years emerged as the powerful tools for solving the large-
scale optimization problems. One of the most popular tensor network is tensor train (TT) …

One of the most important issues for the development of a motor-imagery based brain-
computer interface (BCI) is how to design a powerful classifier with strong generalization …