Automated emotion recognition (AEE) is an important issue in various fields of activities
which use human emotional reactions as a signal for marketing, technical equipment, or …

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

Electroencephalogram (EEG) has been widely used in brain computer interface (BCI) due to
its convenience and reliability. The EEG-based BCI applications are majorly limited by the …

Recognition of emotions based on electroencephalography (EEG) has become one of the
most emerging topics for healthcare, education system, knowledge sharing, gaming, and …

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis,
identifying and comparing key steps of EEG‐based Alzheimer's disease (AD) detection …

Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer
interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual …

Measuring and identifying the specific level of sustained attention during continuous tasks is
essential in many applications, especially for avoiding the terrible consequences caused by …

A classification model (Stress Classification-Net) of emotional stress and physical stress is
proposed, which can extract classification features based on multispectral and tissue blood …

The identification of the emotional states corresponding to the four quadrants of the
valence/arousal space has been widely analyzed in the scientific literature by means of …

Numerous previous studies have proved the enormous potential of high frequency EEG in
emotion recognition, however, the current existing EEG analytic methods are not so effective …