Multi-modal fusion combines multiple modal information to overcome the limitation of
incomplete information expressed by a single modality, so as to realize the complementarity …

Many classical data processing techniques rely on the representation and computation of
vector and matrix forms, where the vectorization or matricization is often employed on …

Data fusion refers to the joint analysis of multiple datasets that provide different (eg,
complementary) views of the same task. In general, it can extract more information than …

Data fusion is the joint analysis of multiple inter-related datasets that provide complementary
views of the same phenomenon. The process of correlating and fusing information from …

Background We developed a gap analysis that examines the role of brain–computer
interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their …

Multi-group fMRI data may possess different types of subjects, tasks, scans, etc. Fortunately,
coupled canonical polyadic decomposition (CCPD) requires multiple tensor datasets to …

Joint Analysis of EEG and fMRI datasets can bring new insight into brain mechanisms. In this
paper, we employed the recently introduced Correlated Coupled Tensor Matrix Factorization …

High-dimensional and multimodal data to describe various aspects of a patient's clinical
condition have become increasingly abundant in the medical field across a variety of …

Plant metabolomics is an important research area in plant science. Chemometrics is a useful
tool for plant metabolomic data analysis and processing. Among them, high-order …

Background: It is crucial to understand the neural feedback mechanisms and the cognitive
decision-making of the brain during the processing of rewards. Here, we report the first …