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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.

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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.

Keywords:
emotion recognitionlong short-term memorymultimodal fusionphysiological signalprincipal component analysisvirtual reality

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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.