In this paper, hybrid brain-computer interface (hBCI) technologies for improving
classification accuracy and increasing the number of commands are reviewed. Hybridization …

Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

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

With the rapid development of deep learning, more and more deep learning-based motor
imagery electroencephalograph (EEG) decoding methods have emerged in recent years …

Virtual reality (VR) has emerged as a powerful tool for rehabilitation. Many effective VR
applications have been developed to support motor rehabilitation of people affected by …

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater
accuracy are highly desirable. To this end, a number of techniques have been proposed …

Motor imagery (MI) is considered to be a promising cognitive tool for improving motor skills
as well as for rehabilitation therapy of movement disorders. It is believed that MI training …

Brain-computer interfaces (BCIs) not only can allow individuals to voluntarily control external
devices, helping to restore lost motor functions of the disabled, but can also be used by …

Varied individual ability to control the sensory-motor rhythms may limit the potential use of
motor-imagery (MI) in neurorehabilitation and neuroprosthetics. We employed …

Brain-Computer interfacing (BCI) has currently added a new dimension in assistive robotics.
Existing brain-computer interfaces designed for position control applications suffer from two …