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Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people's quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research's application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.

Emotional dialogue generation models do not consider the emotional intensity factor in response generation, which leads to the inappropriateness of the emotional expression in generated response, and reduces the user interaction experience. Inspired by the work of emotional intensity in emotional psychology, this paper proposes an emotional intensity response generation model (EIRGM), which includes an emotional intensity prediction unit, a context encoding module and an emotional intensity response generation unit. An emotional intensity prediction unit provides emotion categories and emotional intensity for reply sentences; a context encoding module provides content basis for response; an emotional intensity response generation unit is used to express the emotion and intensity in response. Based on the NLPCC2018 open-domain dialogue dataset, experiments are carried out in terms of emotional appropriateness, emotional intensity appropriateness, content relevance, and dialogue persistence. Experimental results show that EIRGM is not much different from the optimal model in terms of emotional appropriateness, and EIRGM is improved by 4.1 percentage points and 0.8 percentage points compared with the optimal model in terms of emotional intensity appropriateness and dialogue persistence, respectively. It shows that the model improves the emotional intensity appropriateness of emotional expression, and improves the user's willingness to interact. [ABSTRACT FROM AUTHOR]

Emotions affect human physical and mental health, making it important to study emotion monitoring systems and products. This paper discusses the three most crucial modules of emotion monitoring systems including sensors, algorithm models and actuators, providing an overview of their applications in the wearable field. Regarding sensors, this article introduces corresponding sensors for different emotional recognition contents, aiming to provide a way for sensor selection. The focus is on introducing flexible sensors and summarizing the materials used for flexible electrodes. In terms of algorithm models, the most commonly used machine learning and deep learning models are introduced. For actuators, feedback devices are presented for expressing and regulating user emotions based on the human body's five senses. Finally, this article focuses on emotion monitoring products, discusses current issues in the study of emotion monitoring systems, and proposes prospects for the application of emotion monitoring systems in the wearable field, particularly in the textile industry. [ABSTRACT FROM AUTHOR]