Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

Classification of motor imagery electroencephalograph signals is a fundamental problem in
brain–computer interface (BCI) systems. We propose in this paper a classification framework …

Objective The objective of this study was to build a supervised machine learning-based
classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of …

In the field of brain-machine interface (BMI) research, the development of cognitive BMI is a
hot topic because it may lead to more intuitive and goal-directed findings than existing BMI …

This paper puts forward a new method of landscape recognition and evaluation by using
aerial video and EEG technology. In this study, seven typical landscape types (forest …

A key strategy to enable training of deep neural networks is to use non-saturating activation
functions to reduce the vanishing gradient problem. Popular choices that saturate only in the …

Both color and structure make important contributions to human visual perception, as well as
the evaluation of landscape quality and landscape aesthetics. The EEG equipment …

With the rapid development of new energy vehicles, the users have an increasing demand
for charging piles. It is generally believed that the charging pile is a kind of practical product …

Depression is a mental illness that negatively affects a person's thinking, action, and feeling.
Thus the rate of depression is identified by analysing Electroencephalogram (EEG) signals …

Object recognition and object identification are multifaceted cognitive operations that require
various brain regions to synthesize and process information. Prior research has evidenced …