In recent years, with the rapid development of machine learning, automatic emotion
recognition based on electroencephalogram (EEG) signals has received increasing …

Electroencephalography (EEG) signals from each channel mainly reflect activities of the
brain region close to the channel position, and the activities cooperated by various brain …

EEG signals play an important role in both the diagnosis of neurological diseases and
understanding the psychophysiological processes. Classification of EEG signals includes …

Classification of electroencephalogram (EEG) signals is an important task in brain–computer
interfaces applications. This paper combines autoregressive (AR) model and sample …

Phase synchrony assessment across non-stationary multivariate signals is a useful way to
characterize the dynamical behavior of their underlying systems. Traditionally, phase …

Phase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based
BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of …

Cooperation and competition are two common forms of interpersonal interactions and
exploring inter-brain synchronization in these two forms can help to further deliberate the …

Background Electroencephalogram technology provides a reference for the study of
schizophrenia. Constructing brain functional networks using electroencephalogram …

Electroencephalogram (EEG) plays a crucial role in the study of working memory, which
involves the complex coordination of brain regions. In this research, we designed and …

Purpose EEG analysis of emotions is greatly significant for the diagnosis of psychological
diseases and brain-computer interface (BCI) applications. However, the applications of EEG …