基于脑功能网络的虚拟现实晕动症检测
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南京信息工程大学自动化学院

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国家自然科学基金(62206130);江苏省自然科技计划(BK20200821);南京信息工程大学科研启动经费(2020r075);江苏高校教育信息化研究课题(2023JSETKT032)


Virtual Reality Sickness Detection Based on Brain Functional Networks
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School of Automation,Nanjing University of Information Science Technology

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    摘要:

    基于对不同眩晕状态下的脑电信号(EEG)解码提出有效的检测方案,有助于研究虚拟现实晕动症的缓解方法。本文采用多元变分模态分解将EEG划分为五个频段,并根据晕动症量表结果将数据划分为不同眩晕状态组,利用PLV方法计算EEG频段内和频段间的功能连接以构建超邻接矩阵,并基于SVM和CNN等模型进行分类识别。研究结果显示,聚类系数、局部效率和加权节点度三种具有显著性差异的拓扑特征融合后,在眩晕vs.非眩晕,高眩晕vs.低眩晕两个任务中的最高平均分类准确率分别为91.70%和96.00%。此外,本文还将超邻接矩阵直接输入CNN模型,在两个任务中得到的平均分类准确率分别达到93.40%和98.50%。结果表明本研究所提方法可用于虚拟现实晕动症的检测,并为进一步研究晕动症对各脑区功能耦合的影响提供了参考。

    Abstract:

    An effective detection scheme based on decoding electroencephalogram (EEG) signals under different states of sickness is proposed, which is helpful for studying methods to alleviate virtual reality motion sickness (VRMS). This article uses multivariate variational mode decomposition (MVMD) to divide EEG into five frequency bands and divides the data into different sickness state groups based on the results of the motion sickness scale. The phase-locking value (PLV) method is used to calculate the functional connections within and between EEG frequency bands to construct a super adjacent matrix, and classification recognition is performed based on models such as support vector machine (SVM) and convolutional neural network (CNN). The research results show that the fusion of three topological features with significant differences in clustering coefficient, local efficiency, and weighted node degree results in sickness vs Non sickness, high sickness vs The highest average classification accuracy in the two tasks of low sickness was 91.70% and 96.00%, respectively. In addition, this article also directly inputs the super adjacency matrix into the CNN model, achieving average classification accuracies of 93.40% and 98.50% in two tasks, respectively. The results indicate that the method proposed in this study can be used for the detection of motion sickness in virtual reality and provide reference for further research on the impact of motion sickness on the functional coupling of various brain regions.

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杨文清,化成城,殷利平,陶建龙,陈玥池,戴志安,刘佳.基于脑功能网络的虚拟现实晕动症检测[J].南京信息工程大学学报,,():

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  • 收稿日期:2024-11-20
  • 最后修改日期:2025-01-07
  • 录用日期:2025-01-09
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