Exploring Gender Differences in Emotion Recognition with Electroencephalography

Gender has been demonstrated to have a significant impact on emotion response. However, most previous researches on the effects of gender on emotion have largely focused on the fields of psychology and neuroscience. This paper employs electroencephalography signals to investigate gender differences in emotional expressivity, emotional experience and emotion recognition from the perspective of affective computing. Considering the advantages of being concise, comprehensible and emotionally impactful, the short videos are chosen to elicit positive, neutral and negative emotional categories. The analysis results on emotional responses show that the gender differences do exist in evoking emotion during watching short videos. Additionally, we use differential entropy as features and conduct the cross-subject emotion recognition experiment employing support vector machine. The experimental results show that the same gender training strategy outperforms the different gender training one, which implies that there should be gender in-group advantage in electroencephalogram patterns when processing emotions. These findings provide valuable information for a deeper understanding of the role of gender differences in emotion processing and emphasize the importance of integrating neuroscience and computational approaches in emotion recognition tasks.