Convolutional neural networks (CNNs) have recently been applied to electroencephalogram
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …

Electroencephalography (EEG) based motor imagery (MI) is one of the promising Brain–
computer interface (BCI) paradigms enable humans to communicate with the outside world …

Objective. Motor imagery (MI) electroencephalography (EEG) classification is regarded as a
promising technology for brain–computer interface (BCI) systems, which help people to …

Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs)
suffer from the limited number of samples and simplified features, so as to produce poor …

The brain-computer interface (BCI) is a cutting-edge technology that has the potential to
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …

As a challenging topic in brain-computer interface (BCI) research, motor imagery
classification based on electroencephalogram (EEG) received more and more attention …

Motor imagery electroencephalography (MI-EEG) signals are generated when a person
imagines a task without actually performing it. In recent studies, MI-EEG has been used in …

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled
electroencephalogram (EEG) representation method which can preserve not only temporal …

Background Common spatial pattern (CSP) is most widely used in motor imagery based
brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the …

Motor imagery (MI) electroencephalography (EEG) classification is an important part of the
brain-computer interface (BCI), allowing people with mobility problems to communicate with …