This review focuses on electroencephalogram (EEG) acquisition and feedback technology
and its core elements, including the composition and principles of the acquisition devices, a …

The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …

Deep learning for electroencephalogram-based classification is confronted with data
scarcity, due to the time-consuming and expensive data collection procedure. Data …

In Human–Machine interaction, the possibility of increasing the intelligence and adaptability
of the controlled plant by imitating human control behavior has been an objective of many …

A new kind of sequence–to–sequence model called a transformer has been applied to
electroencephalogram (EEG) systems. However, the majority of EEG–based transformer …

The electroencephalogram (EEG) based motor imagery (MI) signal classification, also
known as motion recognition, is a highly popular area of research due to its applications in …

Objective. The combination of the motor imagery (MI) electroencephalography (EEG) signals
and deep learning-based methods is an effective way to improve MI classification accuracy …

Human emotions are complex, mutiifaceted phenomena, defined as multidimensional
subjective experiences influenced by several factors like cognitive processing, social norms …

Brain-computer interfaces based on motor imagery have played important roles in motor
rehabilitation, brain function regulation, disease monitoring, etc. However, due to the low …

Motor imagery brain-computer interfaces (MI-BCIs) play a crucial role in fields such as robot
control and stroke rehabilitation. With the flourishing development of deep learning, there …