Emotion perception and recognition: An exploration of cultural differences and similarities

EEG-based emotion recognition systems; comprehensive study

Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical implications in emotional health care, human-computer interaction, and so on. This paper provides a comprehensive study of different methods for extracting electroencephalography (EEG) features for emotion recognition from four different perspectives, including time domain features, frequency domain features, time-frequency features, and nonlinear features. We summarize the current pattern recognition methods adopted in most related works, and with the rapid development of deep learning (DL) attracting the attention of researchers in this field, we pay more attention to deep learning-based studies and analyse the characteristics, advantages, disadvantages, and applicable scenarios. Finally, the current challenges and future development directions in this field were summarized. This paper can help novice researchers in this field gain a systematic understanding of the current status of emotion recognition research based on EEG signals and provide ideas for subsequent related research.

The Association Between Emotion Recognition and Internalizing Problems in Children and Adolescents: A Three-Level Meta-Analysis

Numerous studies have explored the link between how well youth recognize emotions and their internalizing problems, but a consensus remains elusive. This study used a three-level meta-analysis model to quantitatively synthesize the findings of existing studies to assess the relationship. A moderation analysis was also conducted to explore the sources of research heterogeneity. Through a systematic literature search, a total of 42 studies with 201 effect sizes were retrieved for the current meta-analysis, and 7579 participants were included. Emotion recognition was negatively correlated with internalizing problems. Children and adolescents with weaker emotion recognition skills were more likely to have internalizing problems. In addition, this meta-analysis found that publication year had a significant moderating effect. The correlation between emotion recognition and internalizing problems decreased over time. The degree of internalizing problems was also found to be a significant moderator. The correlation between emotion recognition and internalizing disorders was higher than the correlation between emotion recognition and internalizing symptoms. Deficits in emotion recognition might be relevant for the development and/or maintenance of internalizing problems in children and adolescents. The overall effect was small and future research should explore the clinical relevance of the association.

A time series analysis model of the relationship between psychoacoustic parameters of urban soundscape spatial sequences and emotional changes

A listening test was conducted with 32 participants to obtain data on emotional changes in response to three types of urban soundscape spatial sequences. By establishing a time series model, the relationship between psychoacoustic parameters of the sequence and changes in the two dimensions of emotion was determined. Results showed that psychoacoustic parameters can explain 44% and 40%-49% of the changes in the pleasantness and arousal dimensions of emotion, respectively. Roughness and fluctuation have the highest correlation with emotional changes, while loudness and articulation index have the lowest correlation with emotional changes. This research verified the lags between psychoacoustic changes in the soundscape and the associated perceived emotion. First, there was a 3-4 s lag between psychoacoustic parameters and emotional changes. Second, changes in roughness and loudness could cause synchronous changes in emotions, while other parameters could cause delayed changes in emotions. Finally, the lag of emotion had a strong and stable explanatory power for emotional changes. This research proves the effectiveness of the time series analysis technology in establishing the dynamic relationship between the acoustic parameters of soundscape sequences and the second-by-second perceived emotions and provides a new data analysis method for in-depth study of soundscape sequence perception.

Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals

Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.