Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

Abstract Brain-Computer Interaction (BCI) system intelligence has become more dependent
on electroencephalogram (EEG)-based emotion recognition because of the numerous …

Accurate binary classification of electroencephalography (EEG) signals is a challenging task
for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this …

Recently, electroencephalogram-based emotion recognition has become crucial in enabling
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …

Over recent years, brain-computer interface (BCI) has emerged as an alternative
communication system between the human brain and an output device. Deciphered intents …

Recent technological progress has allowed the development of low-cost and highly portable
brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the …

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been
explored. These include the use of intensive motor training, motor-imagery (MI), and action …

The fields of music, health, and technology have seen significant interactions in recent years
in develo** music technology for health care and well-being. In an effort to strengthen the …

Digital monitoring of physiological signals can allow computer systems to adapt
unobtrusively to users, so as to enhance personalised 'smart'interactions. In recent years …

Cognitive workload is one of the widely invoked human factors in the areas of human–
machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and …