The human nervous system is one of the most complicated systems in nature. Complex
nonlinear behaviours have been shown from the single neuron level to the system level. For …

Detection of mental disorders such as schizophrenia (SZ) through investigating brain
activities recorded via Electroencephalogram (EEG) signals is a promising field in …

The human brain is a complex network of interacting nonstationary subsystems, whose
complicated spatial–temporal dynamics is still poorly understood. Deeper insights can be …

Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer
interaction. However, conventional neural networks have limitations in extracting profound …

People living with schizophrenia (SCZ) experience severe brain network deterioration. The
brain is constantly fizzling with non-linear causal activities measured by …

Deductive formalisms have been strongly developed in recent years; among them, answer
set programming (ASP) gained some momentum and has been lately fruitfully employed in …

The quantification of causality is vital for understanding various important phenomena in
nature and laboratories, such as brain networks, environmental dynamics, and pathologies …

We generated asynchronous functional networks (aFNs) using a novel method called
optimal causation entropy and compared aFN topology with the correlation-based …

The coupling of electroencephalographic (EEG) signals reflects the interaction between
brain regions, which is of great importance for the assessment of motor function in post …

A connectional brain template (CBT) is a normalized representation of a population of brain
multigraphs, where two anatomical regions of interests (ROIs) are connected by multiple …