The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …

Abstract Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel
for communication to those who are suffering from neuronal disorders. The designing of an …

Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …

Deep learning (DL) methods and architectures have been the state-of-the-art classification
algorithms for computer vision and natural language processing problems. However, the …

Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …

Brain-computer interface (BCI) is a system empowering humans to communicate with or
control the outside world with exclusively brain intentions. Electroencephalography (EEG) …

The performance of a classifier in a brain-computer interface (BCI) system is highly
dependent on the quality and quantity of training data. Typically, the training data are …

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

One of the most important issues for the development of a motor-imagery based brain-
computer interface (BCI) is how to design a powerful classifier with strong generalization …