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Virtual reality-induced emotion recognition with deep learning-based multimodal physiological feature fusion.
Xiaoli Fan1, Chaoyi Zhao2, Hua Guo1
1Air Force Medical Center, PLA, Beijing, China.
Frontiers in Psychology
|May 1, 2026
Summary
This study introduces a novel framework using virtual reality (VR) for emotion elicitation and deep learning for accurate emotion recognition. The combined approach significantly improves objective emotion detection compared to traditional methods.
Area of Science:
- Affective computing and human-computer interaction.
- Neuroscience and physiological signal processing.
- Virtual reality and immersive technologies.
Background:
- Conventional emotion recognition methods face limitations in ecological validity, data completeness, and model performance.
- Accurate and objective emotion recognition is crucial for advancing fields like intelligent systems and mental health.
- There is a need for innovative approaches that enhance the realism of emotion elicitation and the precision of recognition.
Purpose of the Study:
- To develop and validate a novel framework for objective emotion recognition.
- To integrate ecologically valid virtual reality (VR) for emotion elicitation with deep learning-based multimodal physiological signal fusion.
- To overcome the limitations of traditional emotion recognition techniques.
Main Methods:
- Developed an immersive VR environment to elicit positive, neutral, and negative emotional states.
- Recorded synchronized physiological signals (EEG, ECG, GSR) and subjective data from 20 participants.
- Employed a hybrid Principal Component Analysis (PCA) with Long Short-Term Memory (LSTM) network after rigorous nested cross-validation and feature selection.
Main Results:
- VR-induced emotions were confirmed effective via subjective evaluations and significant physiological changes (EEG, ECG, GSR).
- The PCA-LSTM model achieved a mean accuracy of 87.18% ± 2.28% in emotion recognition.
- The proposed model significantly outperformed baseline machine learning models like SVM, RF, k-NN, and XGBoost.
Conclusions:
- The integration of an ecologically valid VR paradigm with a multimodal PCA-LSTM fusion model enhances emotion recognition objectivity and accuracy.
- This framework offers a robust solution to the challenges of ecological validity and quantification precision in emotion recognition.
- The study demonstrates potential applications in intelligent human-computer interaction and mental health monitoring.
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