[Research progress on emotion recognition by combining virtual reality environment and electroencephalogram signals]

Abstract

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.

Keywords: Electroencephalogram signal; Emotion recognition; Machine learning; Virtual reality technology.

图 1

Russell's circular emotion classification model 基于Russell的环形情绪分类模型

图 2

Emotion recognition based on EEG signals in VR environment VR环境中基于EEG信号的情绪识别