Bringing emotion recognition (ER) out of the controlled laboratory setup into everyday life
can enable applications targeted at a broader population, eg, hel** people with …

Human emotion recognition, crucial for interpersonal relations and human-building
interaction, identifies emotions from various behavioral signals to improve user interactions …

A systematic review on machine-learning strategies for improving generalization in
electroencephalography-based emotion classification was realized. In particular, cross …

Electroencephalograms (EEGs) are often used for emotion recognition through a trained
EEG-to-emotion models. The training samples are EEG signals recorded while participants …

The accurate detection of emotions has significant implications in healthcare, psychology,
and human–computer interaction. Integrating personality information into emotion …

Objective Recognizing emotions from electroencephalography (EEG) signals is a
challenging task due to the complex, nonlinear, and nonstationary characteristics of brain …

Hazard recognition assisted by human–machine collaboration (HMC) techniques can
facilitate high productivity. Human–machine collaboration techniques promote safer working …

In recent years, with the continuous development of artificial intelligence and brain-computer
interfaces, emotion recognition based on electroencephalogram (EEG) signals has become …

Automated emotion recognition has applications in various fields, such as human-machine
interaction, healthcare, security, education, and emotion-aware recommendation/feedback …

Before fully autonomous driving technology is developed, drivers are not free from the
responsibility of Take-Over Request (TOR). Even with level 5 automation, a driver still can …