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

Area of Science:

• Neuroscience and Psychology
• Human-Computer Interaction
• Virtual Reality Technologies

Background:

• Emotion recognition analyzes human emotional states via voice, facial expressions, and physiological indicators.
• Electroencephalogram (EEG) signals and virtual reality (VR) offer advanced methods for studying emotional changes.
• Existing literature lacks comprehensive reviews on the combined use of EEG and VR for emotion recognition.

Purpose of the Study:

• To comprehensively review and synthesize research on emotion recognition utilizing EEG signals within VR environments over the past five years.
• To provide a foundational understanding of VR and EEG-based emotion recognition theories.
• To analyze key methodologies including emotion induction, feature extraction, and classification.

Main Methods:

• Systematic review and synthesis of scientific literature from the last five years.
• Analysis of studies employing EEG signal acquisition within immersive VR environments.
• Focus on emotion induction techniques, EEG feature extraction algorithms, and classification models.

Main Results:

• Identified and summarized current research trends in EEG-based emotion recognition within VR.
• Detailed the common methods for inducing emotions and extracting relevant EEG features.
• Highlighted various classification approaches applied to recognize emotional states.

Conclusions:

• The integration of EEG and VR shows significant potential for advancing emotion recognition.
• Applications span psychological therapy, education, and training, aiming to improve quality of life.
• Future research directions and development trends are outlined for the field.