Domain Adaptation for Fear of Heights Classification in a VR Environment Based on EEG and ECG

IF 6.9 3区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Andrea Apicella, Pasquale Arpaia, Simone Barbato, Giovanni D’Errico, Giovanna Mastrati, Nicola Moccaldi, Ersilia Vallefuoco, Selina Christin Wriessnegger
{"title":"Domain Adaptation for Fear of Heights Classification in a VR Environment Based on EEG and ECG","authors":"Andrea Apicella, Pasquale Arpaia, Simone Barbato, Giovanni D’Errico, Giovanna Mastrati, Nicola Moccaldi, Ersilia Vallefuoco, Selina Christin Wriessnegger","doi":"10.1007/s10796-024-10484-z","DOIUrl":null,"url":null,"abstract":"<p>Three levels of fear of heights were detected in subjects with different severities of acrophobia, based on the electroencephalographic (EEG) and electrocardiographic (ECG) signals. The study aims to demonstrate the feasibility of a data-fusion-based method for real-time assessment of the fear of heights intensity to integrate into adaptive Virtual Reality Exposure Therapy for acrophobia. The generalization performance of classification tasks on fear states is improved by exploiting both trait-based clustering and Domain Adaptation methods. Participants were gradually exposed to increasing height levels through a Virtual Reality (VR) scenario representing a canyon. The initial severity of fear of heights, the level of distress at each height, and the anxiety level before and after the exposure were assessed through the Acrophobia Questionnaire, the Subjective Unit of Distress, and the State and Trait Anxiety Inventory, respectively. The Simulator Sickness Questionnaire was administered to exclude possible motion sickness interference in the experiment. The EEG and ECG signals were acquired through a 32-channel headset and 1 Lead ECG derivation during the exposure to the eliciting VR scenario. Four classifiers (i.e. Support Vector Machines, Deep Neural Networks, Random Forests, and <i>k</i>-Nearest Neighbors) were adopted in the experimental environment. Preliminary tests were performed in a within-subject experiment, achieving the best classification accuracy of <span>\\(87.1 \\% \\pm 7.8 \\%\\)</span> with a Deep Neural Network. As the cross-subject approach is concerned, three strategies, namely Domain Adaptation (DA), data fusion (combining EEG with ECG), and participant clustering (based on the acrophobia severity), were evaluated. DA resulted in the most effective strategies by determining an improvement of more than 20 % in classification accuracy. Random Forest performed the best classification accuracy for the severe acrophobia cluster with a mean of <span>\\(63.6 \\%\\)</span> and a standard deviation of <span>\\( 13.4 \\%\\)</span> over three classes by exploiting Stratified Normalization.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"18 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems Frontiers","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10796-024-10484-z","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Three levels of fear of heights were detected in subjects with different severities of acrophobia, based on the electroencephalographic (EEG) and electrocardiographic (ECG) signals. The study aims to demonstrate the feasibility of a data-fusion-based method for real-time assessment of the fear of heights intensity to integrate into adaptive Virtual Reality Exposure Therapy for acrophobia. The generalization performance of classification tasks on fear states is improved by exploiting both trait-based clustering and Domain Adaptation methods. Participants were gradually exposed to increasing height levels through a Virtual Reality (VR) scenario representing a canyon. The initial severity of fear of heights, the level of distress at each height, and the anxiety level before and after the exposure were assessed through the Acrophobia Questionnaire, the Subjective Unit of Distress, and the State and Trait Anxiety Inventory, respectively. The Simulator Sickness Questionnaire was administered to exclude possible motion sickness interference in the experiment. The EEG and ECG signals were acquired through a 32-channel headset and 1 Lead ECG derivation during the exposure to the eliciting VR scenario. Four classifiers (i.e. Support Vector Machines, Deep Neural Networks, Random Forests, and k-Nearest Neighbors) were adopted in the experimental environment. Preliminary tests were performed in a within-subject experiment, achieving the best classification accuracy of \(87.1 \% \pm 7.8 \%\) with a Deep Neural Network. As the cross-subject approach is concerned, three strategies, namely Domain Adaptation (DA), data fusion (combining EEG with ECG), and participant clustering (based on the acrophobia severity), were evaluated. DA resulted in the most effective strategies by determining an improvement of more than 20 % in classification accuracy. Random Forest performed the best classification accuracy for the severe acrophobia cluster with a mean of \(63.6 \%\) and a standard deviation of \( 13.4 \%\) over three classes by exploiting Stratified Normalization.

Abstract Image

基于脑电图和心电图的 VR 环境中高地恐惧分类的领域适应技术
根据脑电图(EEG)和心电图(ECG)信号,在患有不同严重程度恐高症的受试者中检测出三种恐高程度。该研究旨在证明基于数据融合的恐高强度实时评估方法的可行性,并将其整合到针对恐高症的自适应虚拟现实暴露疗法中。通过利用基于特质的聚类和领域适应方法,提高了恐惧状态分类任务的泛化性能。参与者通过虚拟现实(VR)场景中的峡谷逐渐接触到越来越高的高度。通过恐高症问卷、主观苦恼单位以及状态和特质焦虑量表分别评估了最初的恐高严重程度、在每个高度的苦恼程度以及暴露前后的焦虑程度。为排除实验中可能出现的晕动病干扰,还进行了模拟器晕动病问卷调查。脑电图和心电图信号通过 32 通道头戴式耳机和 1 导联心电图推导在暴露于诱发 VR 场景期间获取。实验环境中采用了四种分类器(即支持向量机、深度神经网络、随机森林和 k-最近邻)。在主体内实验中进行了初步测试,使用深度神经网络达到了最佳分类准确率(87.1 \% \pm 7.8 \%)。就跨受试者方法而言,评估了三种策略,即领域适应(DA)、数据融合(结合脑电图和心电图)和参与者聚类(基于恐高症严重程度)。DA是最有效的策略,其分类准确率提高了20%以上。随机森林利用分层归一化技术对严重恐高症集群的分类准确率最高,三个等级的平均值为63.6%,标准偏差为13.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Information Systems Frontiers
Information Systems Frontiers 工程技术-计算机:理论方法
CiteScore
13.30
自引率
18.60%
发文量
127
审稿时长
9 months
期刊介绍: The interdisciplinary interfaces of Information Systems (IS) are fast emerging as defining areas of research and development in IS. These developments are largely due to the transformation of Information Technology (IT) towards networked worlds and its effects on global communications and economies. While these developments are shaping the way information is used in all forms of human enterprise, they are also setting the tone and pace of information systems of the future. The major advances in IT such as client/server systems, the Internet and the desktop/multimedia computing revolution, for example, have led to numerous important vistas of research and development with considerable practical impact and academic significance. While the industry seeks to develop high performance IS/IT solutions to a variety of contemporary information support needs, academia looks to extend the reach of IS technology into new application domains. Information Systems Frontiers (ISF) aims to provide a common forum of dissemination of frontline industrial developments of substantial academic value and pioneering academic research of significant practical impact.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信