Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

Neuromarketing has become an academic and commercial area of interest, as the
advancements in neural recording techniques and interpreting algorithms have made it an …

Virtual reality (VR) applications, especially those where the user is untethered to a computer,
are becoming more prevalent as new hardware is developed, computational power and …

Convolutional neural networks (CNNs) are widely used to recognize the user's state through
electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually …

Here we present a robust physiological model for the recognition of human emotions, called
Deep Physiological Affect Network. This model is based on a convolutional long short-term …

The bulk of social neuroscience takes a 'stimulus-brain'approach, typically comparing brain
responses to different types of social stimuli, but most of the time in the absence of direct …

Recently, due to the emergence of mobile electroencephalography (EEG) devices,
assessment of mental workload in highly ecological settings has gained popularity. In such …

Motor imagery (MI) based brain-computer interface (BCI) facilitates a medium to translate the
human motion intentions using Motor imagery electroencephalogram (EEG) into control …

Abstract The evolution of Autonomous Vehicles (AVs) has blurred the distinction between
drivers and passengers, resulting in increased demand for in-car entertainment …

Complex emotion is an aggregate of two or more others which has highly variable
appearances, inter-dependence, and affective dynamics. These properties make the …