Background Human emotions greatly affect the actions of a person. The automated emotion
recognition has applications in multiple domains such as health care, e-learning …

Emotion recognition based on electroencephalography (EEG) signal features is now one of
the booming big data research areas. As the number of commercial EEG devices in the …

Emotion recognition using EEG has been widely studied to address the challenges
associated with affective computing. Using manual feature extraction methods on EEG …

This paper proposes emotion detection using Electroencephalography (EEG) signal based
on Linear Formulation of Differential Entropy (LF-D f E) feature extractor and BiLSTM …

Affective, emotional, and physiological states (AFFECT) detection and recognition by
capturing human signals is a fast-growing area, which has been applied across numerous …

The purpose of this research is to use a cross-dataset approach to construct an EEG-based
emotion recognition system. So far, numerous modeling strategies for emotion recognition …

In face-to-face and online learning, emotions and emotional intelligence have an influence
and play an essential role. Learners' emotions are crucial for e-learning system because …

Various lengths of time window have been used in feature extraction for
electroencephalogram (EEG) signal processing in previous studies. However, the effect of …

Gunshot sounds are common in crimes, particularly those involving threats, harassment, or
killing. The gunshot sounds in crimes can create fear and panic among victims, often leading …

In this study, cognitive and behavioral emotion regulation strategies (ERS) are classified by
using machine learning models driven by a new local EEG complexity approach so called …