… Furthermore, the EEG rhythms that are highly correlated with emotions are … emotions is
discussed. Finally, we compare different ML and deep learning algorithms for emotion recognition …

… This paper proposes an approach for emotion recognition using biosignal processing
and machine learning techniques. We developed an instrumented glove with two off-the-shelf …

… Under those circumstances, in this paper, we will focus on emotion recognition using movies
or films. A new approach for emotion elicitation that has emerged in the last two decades, is …

… emotion recognition (SER) systems. Theoretical definition, categorization of affective state and
the modalities of emotion … Several machine learning paradigms were used for the emotion …

… emotion recognition by analyzing key facial regions using principal component analysis and
neural networks. Emotion recognition in … Therefore, classification and emotion recognition is …

… Figure 2 shows the whole process of the ML speech emotion recognition used in this study.
First, an emotional speech database is used, which consists of simulated and annotated …

… In this paper, an emotion recognition system is developed based on the valence-arousal
model. We applied wavelet transform on EEG signals, then energy and entropy were extracted …

… , recognize, and analyze emotions. This study reviews emotion recognition methods that rely
on … It also provides an overview of the datasets and methods used to elicit emotional states. …

… Although the research-grade EEG devices remain preferable for emotion recognition in …
and investigated via machine learning algorithms to predict emotion recognition patterns and …

… Often, learned emotion models are trained on data sets that may not sufficiently … emotion
recognition as a representative scenario to evaluate an approach to improving recognition …