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Related Experiment Video

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How to Study Placebo Responses in Motion Sickness with a Rotation Chair Paradigm in Healthy Participants
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VR motion sickness recognition by using EEG rhythm energy ratio based on wavelet packet transform.

Xiaolu Li1, Changrong Zhu1, Cangsu Xu2

  • 1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, China.

Computer Methods and Programs in Biomedicine
|December 23, 2019
PubMed
Summary

This study introduces a new method using electroencephalogram (EEG) energy ratios to detect virtual reality motion sickness (VRMS). The approach shows high accuracy for individuals and improved applicability across multiple subjects.

Keywords:
Electroencephalogram (EEG)Rhythm energy ratioSupport vector machine (SVM)VR motion sickness (VRMS)Wavelet packet transform (WPT)k-Nearest neighbor classifier (k-NN)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Virtual Reality Technology

Background:

  • Virtual reality motion sickness (VRMS) significantly impedes virtual reality (VR) technology advancement.
  • Current electroencephalogram (EEG)-based VRMS detection methods lack broad applicability across diverse subjects.

Purpose of the Study:

  • To develop a robust EEG feature extraction method for VRMS recognition.
  • To enhance the multi-subject applicability of VRMS detection.

Main Methods:

  • Utilized wavelet packet transform (WPT) for feature extraction from EEG delta, theta, alpha, and beta rhythms.
  • Employed elliptical band-pass filtering and fixed-window segmentation for EEG signal processing.
  • Applied k-Nearest Neighbor (k-NN), polynomial Support Vector Machine (polynomial-SVM), and radial basis function Support Vector Machine (RBF-SVM) for VRMS classification.

Main Results:

  • Achieved an average single-subject VRMS recognition accuracy of 92.85% with polynomial-SVM using a 4-s window.
  • Demonstrated a multi-subject VRMS recognition accuracy of approximately 79.25% for 18 subjects.

Conclusions:

  • The proposed EEG energy ratio method offers superior VRMS recognition accuracy compared to existing techniques.
  • This method exhibits enhanced applicability for multi-subject VRMS detection.
  • Polynomial-SVM outperformed k-NN and RBF-SVM in VRMS recognition using EEG rhythm energy ratios.