The combined effect of fundamental results about neurocognitive processes and
advancements in decoding mental states from ongoing brain signals has brought forth a …

Background In cognitive neuroscience the potential of deep neural networks (DNNs) for
solving complex classification tasks is yet to be fully exploited. The most limiting factor is that …

The brain–computer interface (BCI) technology has received lots of attention in the field of
scientific research because it can help disabled people improve their quality of life. Steady …

We present a survey of psychophysiology-based assessment for quality of experience (QoE)
in advanced multimedia technologies. We provide a classification of methods relevant to …

Most event-related potential (ERP)-based brain-computer interface (BCI) spellers primarily
use matrix layouts and generally require moderate eye movement for successful operation …

Objective. Most existing brain–computer interface (BCI) designs based on steady-state
visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (eg,< 20 Hz) to …

Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface
(BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical …

As the brain is a unique biological system that reflects the subtle distinctions in the mental
attributes of individual humans, electroencephalographic (EEG) signals have been regarded …

With stereoscopic displays a sensation of depth that is too strong could impede visual
comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to …

Brain-computer interfaces (BCIs) aim to directly bridge the human brain and the outside
world through acquiring and processing the brain signals in real time. In recent two decades …