A brain–computer interface (BCI) enables a user to communicate with a computer directly
using brain signals. The most common noninvasive BCI modality, electroencephalogram …

Abstract A motor imagery EEG (MI-EEG) signal is often selected as the driving signal in an
active brain computer interface (BCI) system, and it has been a popular field to recognize MI …

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-
invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL) …

Currently, most of the high-performance models for frequency recognition of steady-state
visual evoked potentials (SSVEPs) are linear. However, SSVEPs collected from different …

Identifying meaningful brain activities is critical in brain-computer interface (BCI)
applications. Recently, an increasing number of neural network approaches have been …

Brain–computer interfaces (BCIs) suffer from a lack of classification accuracy when the
number of electroencephalography (EEG) trials is low. This is therefore during the learning …

In recent years, the success of deep learning has driven the development of motor imagery
brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG). However …

Recent years have witnessed a growing interest in EEG-based wearable classifiers of
emotions, which could enable the real-time monitoring of patients suffering from …

Objective. Motor imagery electroencephalography (MI-EEG) produces one of the most
commonly used biosignals in intelligent rehabilitation systems. The newly developed 3D …

Brain-computer interfaces (BCIs) using Electroencephalography (EEG) have drawn attention
to providing alternative control pathways for users with motor disabilities or even the general …