1.南京信息工程大学自动化学院 南京 210044; 2.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
TP391 TH782
江苏省自然科学基金项目（BK20200821，BK20170955）、南京信息工程大学人才启动经费项目（2020r075）资助
Chai lining
1.School of Automation,Nanjing University of Information Science & Technology,Nanjing 210044,China;2.Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot(C-IMER), Nanjing University of Information Science & Technology,Nanjing 210044,China.Hua chengcheng
1.School of Automation,Nanjing University of Information Science & Technology,Nanjing 210044,China;2.Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot(C-IMER), Nanjing University of Information Science & Technology,Nanjing 210044,China.Zhou zhan feng
1.School of Automation,Nanjing University of Information Science & Technology,Nanjing 210044,China;2.Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot(C-IMER), Nanjing University of Information Science & Technology,Nanjing 210044,China.1.School of Automation,Nanjing University of Information Science & Technology,Nanjing 210044,China;2.Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot(C-IMER), Nanjing University of Information Science & Technology,Nanjing 210044,China.
如今，虚拟现实（VR）技术已在各个领域得到广泛应用，但很多VR系统在带给用户沉浸式体验时会使其产生一种不适症状——虚拟现实晕动症。为弄清虚拟现实晕动症对大脑神经活动的作用，本研究招募被试者利用头戴式VR体验虚拟现实晕动症诱发场景，记录体验前及过程中被试者的脑电信号。采用样本熵及功率谱方法提取不同状态下被试者脑电特征，并进行显著性检验。从全频段来看，在电极F8、F12、CZ、CPZ及OZ处的样本熵均值具有显著性差异，在电极F7、T7及T8处的功率谱均值具有显著性差异（p<0.01）；从分频段来看，在Delta及Theta频段的样本熵均值及功率谱均值同时具有显著性差异（p<0.01）。研究结果表明，样本熵与功率谱分析结果可能与虚拟现实晕动症有关，有望成为度量虚拟现实病的有效指标。
Nowadays, virtual reality (VR) technology has been widely used in various fields, but many VR systems will cause users to have an uncomfortable symptom when they bring immersive experience -- Virtual reality motion sickness. In order to understand the effects of virtual reality motion sickness on brain neural activity, this study recruited subjects to experience virtual reality motion sickness induced scenes by head-mounted VR, and recorded their EEG signals before and during the experience. Sample entropy and Power spectrum were used to extract EEG characteristics of subjects in different states, and significance test was carried out. In the whole frequency band, the mean values of Sample entropy at electrodes F8, F12, CZ, CPZ and OZ were significantly different, and the mean values of Power spectrum at electrodes F7, T7 and T8 were significantly different (P<0.01). In terms of frequency bands, the mean values of Sample entropy and Power spectrum in Delta and Theta bands were significantly different (P<0.01). The results show that the Sample entropy and Power spectrum analysis results may be related to virtual reality motion sickness, which is expected to be an effective index to measure virtual reality sickness.
柴立宁,化成城,周占峰.基于脑电样本熵功率谱的VR诱发晕动症分析[J].电子测量技术,2022,45(20):43-52
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