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基于脑电节律能量与模糊熵的VR诱发晕动症水平检测研究

周占峰 化成城 柴立宁 严颖 刘佳 付荣荣

数据采集与处理2024,Vol.39Issue(2):490-500,11.
数据采集与处理2024,Vol.39Issue(2):490-500,11.DOI:10.16337/j.1004-9037.2024.02.021

基于脑电节律能量与模糊熵的VR诱发晕动症水平检测研究

Detection of VR-induced Motion Sickness Levels Based on EEG Rhythm Energy and Fuzzy Entropy

周占峰 1化成城 1柴立宁 1严颖 2刘佳 1付荣荣3

作者信息

  • 1. 南京信息工程大学自动化学院,南京 210044||南京信息工程大学江苏省智能气象探测机器人工程研究中心,南京 210044||南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京 210044
  • 2. 南京信息工程大学自动化学院,南京 210044
  • 3. 燕山大学河北省测试计量技术及仪器重点实验室,秦皇岛 066000
  • 折叠

摘要

Abstract

Motion sickness has been a key factor affecting the virtual reality user experience and limiting the growth of the virtual reality industry.To address this issue,this paper investigates the effects of virtual reality motion sickness on neural activity in the brain and uses electroencephalogram(EEG)features to detect levels of motion sickness.To obtain features that can measure the level of vertigo,this paper records the EEG signals of subjects before and during the experience of the vertigo test scene,calculates the rhythm energy and fuzzy entropy,uses statistical analysis for feature selection,and finally classifies and verifies the validity of the features.The results show that the energy in the θ and α bands of CP4 and Oz and the energy in the β and γ bands of C4 are significantly reduced when subjects develop motion sickness(p<0.01);in terms of fuzzy entropy,there are significantly higher values of FC4 and Cz fuzzy entropy in the δ band(p<0.000 1)and significantly lower values of O1 fuzzy entropy in the β band(p<0.000 1).Compared to linear discriminant analysis(LDA),logistic regression(LR)and support vector machine(SVM),K nearest neighbor(KNN)shows better classification results with 89%and 91%classification accuracy on rhythm energy and fuzzy entropy,respectively.This study shows that EEG rhythm energy and fuzzy entropy are expected to be effective indicators for motion sickness level detection,providing an objective basis for studying the causes of virtual reality motion sickness and mitigation options.

关键词

虚拟现实/晕动症/脑电信号/模糊熵

Key words

virtual reality/motion sickness/electroencephalogram(EEG)signal/fuzzy entropy

分类

信息技术与安全科学

引用本文复制引用

周占峰,化成城,柴立宁,严颖,刘佳,付荣荣..基于脑电节律能量与模糊熵的VR诱发晕动症水平检测研究[J].数据采集与处理,2024,39(2):490-500,11.

基金项目

国家自然科学基金(62206130,62073282) (62206130,62073282)

江苏省自然科学基金(BK20200821) (BK20200821)

河北省自然科学基金(F2022203092) (F2022203092)

南京信息工程大学人才启动经费(2020r075). (2020r075)

数据采集与处理

OA北大核心CSTPCD

1004-9037

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