Feature extraction of electroencephalography (EEG) signals plays a significant role in the
wearable computing field. Due to the practical applications of EEG emotion calculation …

EEG signal has been widely used in emotion recognition. However, too many channels and
extracted features are used in the current EEG-based emotion recognition methods, which …

In recent years, with the continuous development of artificial intelligence and brain‐
computer interface technology, emotion recognition based on physiological signals …

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

As a high-level function of the human brain, emotion is the external manifestation of people's
psychological characteristics. The emotion has a great impact on people's personality and …

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests
in recent years and has been broadly adopted in medical, affective computing, and other …

EEG-based emotion recognition has been studied for a long time. In this paper, a new
effective EEG feature named differential entropy is proposed to represent the characteristics …

Identification of human emotion involving electroencephalogram (EEG) signals has become
an emerging field in health monitoring application as EEG signals can give us a more …

Emotion can be classified based on 2-dimensional valence-arousal model which includes
four categories of emotional states, such as high arousal high valence, low arousal high …

Emotion recognition is a challenging problem in Brain‐Computer Interaction (BCI).
Electroencephalogram (EEG) gives unique information about brain activities that are created …